Evidence-based design recommendations for prevalence studies on multimorbidity: improving comparability of estimates.

Our analyses identified the following variables as highly significant: “mean age,” “number of age groups”, and “data reporting quality” (all p < 0.0001). “Setting,” “disease classification,” and “number of diseases in the classification” were significant (0.01 < p ≤ 0.03), and “data collection period” and “data source” were non-significant.

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This result states that with all other variables in the model, the additional contribution of the variable “setting” to an optimal fit is barely significant.

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