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The models included variables that were established as confounders [17].
Many models included variables related to spatial relationships of structures.
Initial multivariate models included variables captured for at least 75% of participants with p≤0.10 in univariate analysis.
Multivariable regression models included variables listed in 1st column.
a Logistic Regression models included variables listed in 1st column.
The models included variables that were established as confounders [ 17].
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Finally, the models include variables pertaining to the use of SO2.
Both our matching and DiD models include variables of economic development (e.g., number of convenience and grocery stores, proportion of the population with income below poverty etc).
Subsequent multiple logistic regression models including variables with p < 0.2 from the univariate analysis were calculated.
In addition, the models including variables from both BC and HDR show a better fit taking complexity into account.
All regression models include variables to explain the weekly or monthly seasonality and secular trends of the dependent variable and to account for the impact of influenza activity.
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