Author's Response to Reviews Yours Sincerely

Title: Patterns of Long Term Care in 29 European countries: evidence from an exploratory study. We would like to thank you for revising our manuscript. We also thank the reviewers for their critical comments, which helped us to improve our article significantly. Please, find below our point to point response to the comments. Responses to Editorial requests We thank you for having reminded us to add the Acknowledgement section to our article (see Acknowledgement page 15). authors Alessandro Solipaca and Alessandra Burgio (please see Authors' contribution). Authors did not receive any funding for the study. Responses to Reviewer 1 (Jouke van der Zee) We would like to thank you for your critical comments, which give us the opportunity to improve several aspects of our manuscript. Please, find below our point to point response to your comments. Comment: My first general comment about the study is that it contains too much information. It is both a mostly (that is: on this large scale) unexplored area (so, one would expect the authors to use room for questions about definitions, validity of data , methods, interpretation) and it contains a combination of cross-section and time series data, which, in my opinion, might deserve two separate papers. One paper about 'Patterns' and one about ' Trends'. For the 'Patterns' part one could perhaps present one (as robust as possible) figure per variable per country, while the 'Trends' part would show whether the countries move into a certain direction and which direction that would be and what possible explanation the authors see. Only if the 'Patterns' part could not be constructed without the author's use of time series data this general comment is not adequate, but it would make the paper and the line of reasoning far easier to understand. If the editors/authors decide to keep the paper as it is, I'd suggest a clear structure (in the first part we deal with crossectional differences, in the second part we'll analyze trends. Response: We agree with your observations. We would like to highlight that, as showed in the Methods of the study, the statistical analysis is generated from time series data, more specifically, from a 3D matrix I * J* K , where I are variables, J countries and K years. Principal Components are selected and patterns are defined through a global Principal Component Analysis (PCA) performed on a general matrix after separated PCAs on …