Next, we examined geographic distribution of the policies. First, for each state, we tabulated the mean number of policies implemented by counties in that state in each of the three policy domains. We then produced heatmaps for each state, documenting the number of policies present in each county . For these plots, policies were coded as binary .We next conducted several secondary analyses to account for possible bias introduced by the imputation process.
Second, to allow for greater variation and nuance in policy landscapes, we calculated an overall index of comprehensiveness, rather than simply the number of policies implemented. For this analysis, if there was no policy, this was coded as 0, the most comprehensive were coded as 1, and intermediate categories were fractions thereof. For example, for public events, no restriction was coded as 0, minimal limitations was 0.25, major limitations was 0.50, recommended cancellation was 0.
Finally, we used principal component analysis as an alternative technique to create a composite index of policy comprehensiveness . We also examined the contributing policies to each of the principal components created by this technique to assess the relationships between the different policies in a different manner than the pairwise correlations described above.Overall, policy data were most readily available for states and urban and suburban counties.
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