We thank the editor and the reviewers for their constructive and positive comments, all addressed below in our point-by-point reply.

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Reply: We have checked the entire reference list and made adjustments if necessary.

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Reply: We have added information to the methods section to describe the informed consent procedure as clearly as possible.

Revised sentences:

Ethics statement

This study was approved by the Medical Ethical Committee of the University of Maastricht (METC 2018-0698 and 2019-1035). Invitees were previous participants in the ‘Dutch Health Monitor’, which is a population-based survey of the Public Health Services South and North Limburg. Invitations were only sent to participants who had given permission (and their email-address) in the Dutch health monitor to be invited for future research. Before starting the questionnaire of the current SaNAE study, participants first gave electronic informed consent.

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Reply: This is not applicable

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Reply: Indeed, the results are already in the paper. We rephrased the sentence and referred to table 3. Revised sentence: While the first possibility (fear) was not measured in our study, the latter two were considered unlikely to explain our results, as both multivariate models were adjusted for respondents’ self-reported respiratory infections.

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Data Availability Statement: Data in this study contain potentially identifying and sensitive patient information. Due to the General Data Protection Regulation, it is not allowed to distribute or share any personal data that can be traced back (direct or indirect) to an individual. Moreover, publicly sharing the data would not be in accordance with participant consent for this study. Therefore, interested researchers should contact the head of the data-archiving of the Public Health Service South Limburg (Helen Sijstermans: helen.sijstermans@ggdzl.nl) when they would like to re-use data.

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Review Comments to the Author

Reviewer #1: In this study, Steijvers et al. explored the role of social networks in non-pharmaceutical infection preventive behaviours, especially for respiratory infectious diseases. By collecting data from a very large sample in the pre-COVID era, authors reported that to practice preventive behaviours was independently increased by social network aspects that reflected ‘weak relationships’. The study performed a through analysis of different social network aspects and I believe that the analysis is properly performed. Only few points need to be addressed before the paper can be suitable for publication.

1. Introduction is long. It can be summarizes by deleting the “The composition of social networks can be […] among middle aged and older adults in the Netherlands” paragraph;

Reply: We agree that the introduction is long and have shortened several paragraphs. However, we choose to not completely delete the section on network composition and the theory of strong and weak ties, because this is an important part of the introduction. This study is one of the first that aimed to investigate multiple social network characteristics (both structural (size, type of relation, proximity, mode of contact) and functional aspects (support)) and defined strong and weak relationships based on these characteristics. It is therefore of utmost importance that first a proper description of the social network characteristics is given. In addition, the theory of strong and weak ties is explained. This theory explains that especially the weak ties (geographically, practically, or emotionally more distant relationships) are important in providing informational support. Giving the reader a detailed description of the characteristics and theory in the introduction, contributes to a better understanding of defining the network characteristics into strong or weak relationships, and better interpretation of the found associations.

2. Statistical analysis methods are not clear. I would kindly ask to rephrase the whole paragraph as to better define, for each model the dependent and independent variable for sake of clarity. Also, it is not clear to me why authors entered in a step-wise regression some variables resulting from the previous model, and other ‘confounding founders’. What are these factors? How you chose them?

Reply: We agree that the statistical analyses paragraph can be written more clearly. See below for the revised text.

Revised paragraph:

Participants with missing values on any of the outcome or other variables (n=16) were excluded in analyses. Ordinal logistic regression analyses were performed for the count variable of preventive behaviors as outcome and the network characteristics as independent variables. Binary logistic regression analyses were performed for the four preventive behaviors (four outcomes) separately, with the network characteristics as independent variables, to gain more detailed insight into the specific behaviors. Multivariate logistic (for the separate preventive behaviors) and multivariate ordinal (for the count variable on behaviors) regression analyses were performed, calculating odds ratios (OR) with 95% confidence intervals. Multivariate model (I) included as independent variables the social network characteristics, and confounders (see below). Then, a multivariate model (II) was created including all social network aspects that were associated with the outcome (by a p-value <0.05) in model I and including confounders. To obtain the final model (II), we used a stepwise backward method for the social network variables (while keeping the confounders in the model) [25]. Confounding variables were sociodemographic factors (sex, age, educational level) [26–29], chronic conditions (depression, type II diabetes mellitus, cardiovascular diseases, asthma/COPD) [24,30–35], lifestyle factors (BMI, current smoking) [5,36] and self-reported upper and lower respiratory infections in the previous two months [25,37], as these variables previously were found to be associated with the dependent and independent variables. A priori, collinearity between network aspects was checked and ruled out (all correlations <0.66). A p-value < 0.05 was considered statistically significant. All analyses were performed using IBM SPSS Statistics (version 26.0).

3. In table 2, I do not foresee any advantage in reporting different levels of p-value. As author stated in the statistical section that 0.05 is the statistical significance value they should only report significant vs non-significant values;

Reply: We agree that the confidence intervals around the risk estimates are most informative, and thus we presented these in the Tables. We also included the p-values in the tables to provide the supporting statistics for additional clarification, and therefore like to keep these in the Tables. We agree that the description in the manuscript text in the results section could be more concise and there we reported only significant vs non-significant values in the results section.

Reviewer #2: Interesting and innovative article showing how social networks potentially can increase the effectiveness and can promote healthy behaviors, taking advantage from social relationship and experience from peers. Social media can help people with health concerns or chronic disease, finding in Web communities a comforting source for information exchange, debate, and knowledge enrichment. I suggest the authors to implement Discussion with previous reports demonstrating this value (see Lavorgna L et al. Interact J Med Res. 2017) for health care organizations and beneficial repercussion reaching institutional stakeholders.

Resort to on-line survey has been widely amplified during pandemic due to need of reach a large population with a digital approach. Authors could emphasize the importance and usefulness of this easy and well accepted tool in the Conclusion.

See below for revised text:

Specifically, the strengthening of the so called ‘weak relationships’, as the modes of interactions that provide informational support, that do not have to be in-person, thus possibly via online channels, is worth to be explored further. Such online contact opportunities may especially be useful in the current situation, where the COVID-19 pandemic led to widespread application of isolation, quarantine, and physical distancing, already likely shifting the use of these modes of interactions. Previous studies have shown that the internet can be used to receive social support from or social interaction with others [30,31].This reasoning leads to exploring the promotion of new/linking to already existing online, interactive, informal information platforms that allow people to exchange information and advice. Existing public health websites may be included because these provide information about the reasons why preventive behavior can be important and how to apply preventive measures; interacting with social network members contributes to increasing awareness of one’s behavior. Health care organizations could play an important role in such platforms, as they can facilitate validated information [31]. Future studies need to investigate how to promote such interactive, online platforms.

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