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Exposure at the current day (lag 0), a 2-day average of lag 0 and lag 1 days (lag 0 1), and a 5-day average of lag 0 to lag 4 days (lag 0 4) were examined.
For every pollutant the following lags were evaluated: lag 0, 1, 2 and the average of lag 0 6 days.
This association was stronger with a 2-day moving average of lag 0 + 1 personal NO2 (not shown; −1.75%; 95% CI, −2.83 to −0.673%).
However, for Bangkok the longer cumulative average of lag 0 4 days generated the highest excess risk for all of the pollutants except SO2.
To model the effects of mean temperature we used five lag periods: the average of lag days 0-1, 2-7, 8-14, 15-21, and 22-28.
The variable with the best model fit in the exploratory analysis for ozone was an average of lag 0, 1 and 2 days.
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drop (average of lags 0 13 mean temperature) below a threshold of 21°C [ 6].
We calculated parametric effect estimates for the average of lags 0 1, 2 15 and 15 27 (table 3).
We controlled for PM10 (moving average of lags 0 7), O3 (moving average of lags 0 7) and relative humidity (moving average of lags 0 7) using a natural cubic spline with 3 degrees of freedom (df), as these variables are potential confounders of the association between temperature and mortality [ 14- 16].
Based on the results of the distibuted lag model we then fit a models with a single smooth term representing the average of lags 0 20 mean temperature.
Therefore, in our primary analysis of PM2.5, we examined two different a priori lag structures: a 2-day average of lags 0 and 1 (lag 01) and a single-day lag of 2 days (lag 2).
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