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Hence, the missing category contributed to variation in the analyses, but was not interpreted in the results.
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Press releases and tweets are now issued for every missing person who falls into the "critical missing" category.
*P-values do not include missing category.
We thus added a missing category to these variables.
Adding missing data as a separate category produced an RPR for CAs of 1.1 (95% CI: 0.6 2.1) for this missing category.
For other control variables, we created a "missing" category for missing data.
This was done by creating a missing category for the missing values of each variable.
The missing information for the confounding factors was coded as a missing category for data analysis.
These missing values were considered as a missing category in the statistical analyses.
For the rest of variables, percentage was calculated with missing category included as a separate category.
Due to a larger number of missing LICO values (8.0%), a missing category was included for this variable.
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