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Cross-sectional data sets typically contain observations for a single time point, and the application of anonymization methods is generally straightforward (eg, using the guidelines presented by Templ et al [6]).
In the following paragraphs, the extension to longitudinal information, in particular to status variables (eg, occupation or education), for which the observed values (can) change over time, is discussed. Table 1 shows the problem of using a toy data set with 2 individuals in a simplified manner.
JMIR Public Health Surveill 2022;8(9):e34472
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