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Table 5 Mann-Kendall trend test of annual temperature H0: There is no trend Temperature No. years Kendall's (S) Var.
As an alternative approach we have made the adjustments for seasonality, long term trend, temperature and humidity within the regressions.
In Amsterdam, the model included linear terms for time trend, temperature (lag 2), relative humidity (lag 3), and barometric pressure.
As potential confounders, we considered a global time trend, temperature, relative humidity, barometric pressure, and indicators for month of the year, weekday and holidays.
We considered the following confounder variables: an indicator variable for each subject, long-term time trend, temperature, relative humidity, barometric pressure, and weekday of the visit.
The basic model included a dummy for each subject; linear terms for time trend, temperature (lag 3), and relative humidity (lag 3); and a penalized spline (df = 3) for change in heart rate during the exercise test.
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Natural spline smoothers were used for time trends, temperature, and relative humidity.
Our estimated effect was relatively robust to the confounding factors such as seasonality, long-term trends, temperature, and other pollutants.
We then adjust the de-seasonalized death rates for trends, temperature and humidity by means of regression.
Confounding was adjusted for by adding weekly age-specific positive isolate numbers of other respiratory viruses (RSV, adenovirus and parainfluenza), long term and seasonal trends, temperature and relative humidity, as previously described [ 10].
Figure 18 Long-term trend in temperature at Frankfurt Airport.
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