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We calculated tests for trend by including in Cox models a variable constructed from estimates of the number of years of education for each category of educational attainment.
In the final models, a variable with a P-value <0.05 was considered statistically significant.
In these models, a variable indicating whether the glucose determination was made in the fasting state (yes or no) was also included.
In the multiplicative interaction models, a variable was considered an effect modifier if the P value for the interaction terms was less than or equal to 0.05.
We also tested the potential interaction between O3-8hr and temperatures variables, and we introduced in the models a variable taking into account humidity levels (maximum daily relative humidity).
In addition to analyzing Study I and Study II separately, we tested for heterogeneity of results between studies, and when warranted, carried out analyses on the pooled datasets; for this, we added to the models a variable for the study (I or II).
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I assumed general critical-failure time probability density functions to model a variable hazard rate with a bathtub curve.
In the final model a variable with a P-value ≤ 0.05 was considered statistically significant and retained in the model.
b P value for trend obtained by including in the model a variable representing the median value for each tertile.
To remain in the final model, a variable had to be significant at α of 0.05 at all levels.
To be included in this model, a variable was considered when presented a p value less than 0.05 in univariate analysis.
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