Sensitive attributes for fairness in this dataset would be those that could potentially lead to biased decision-making or discriminatory practices. Here are a few possibilities:

1. `case:citizen`: Using a citizen's status as a parameter in decision-making could be considered discriminatory, especially if it leads to different treatment or outcomes for people based on this factor.

2. `case:gender`: Gender is often viewed as a sensitive attribute because unfairly treating individuals based on their gender would be discriminatory. 

3. `case:german speaking`: If language proficiency affects the services provided or the results obtained, it could lead to biased outcomes.

4. `case:private_insurance`: If those with private insurance are treated differently (preferentially or otherwise) than those without, this can create inequities in outcomes.

5. `case:underlying_condition`: If individuals with underlying conditions receive essentially different treatment from those without (beyond what is medically necessary), it could be seen as unfair.

It should be noted that the sensitivity of an attribute can depend on how it is used. An attribute might be used in a way that is fair in one context but unfair in another. It should also be noted that this list isn't exhaustive  there can be other attributes in a dataset that may raise fairness concerns depending on context.