Social networks in relation to self-reported symptomatic infections in individuals aged 40-75 - the Maastricht study -

Stephanie Brinkhues, Miranda T. Schram, Christian J. P. A. Hoebe, Mirjam E. E. Kretzschmar, Annemarie Koster, Pieter C. Dagnelie, Simone J. S. Sep, Sander M. J. van Kuijk, Paul H. M. Savelkoul, Nicole H. T. M. Dukers-Muijrers

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Background: Most infections are spread through social networks (detrimental effect). However, social networks may also lower infection acquisition (beneficial effect). This study aimed to examine associations between social network parameters and prevalence of self-reported upper and lower respiratory, gastrointestinal and urinary tract infections in a population aged 40-75. Methods: In this population-based cross-sectional cohort study (N = 3004, mean age 60.0 ± 8.2 years, 49% women), infections within the past two months were assessed by self-administered questionnaires. Social network parameters were assessed using a name generator questionnaire. To examine the associated beneficial and detrimental network parameters, univariable and multivariable logistic regression was used. Results: Participants reported an average of 10 people (alters) with whom they had 231 contacts per half year. Prevalences were 31.1% for upper respiratory, 11.5% for lower respiratory, 12.5% for gastrointestinal, and 5.7% for urinary tract infections. Larger network size, and a higher percentage of alters that were friends or acquaintances were associated with higher odds of upper respiratory, lower respiratory and/or gastrointestinal infections (detrimental). A higher total number of contacts, higher percentages of alters of the same age, and higher percentages of family members/acquaintances were associated with lower odds of upper respiratory, lower respiratory and/or gastrointestinal infections (beneficial). Conclusion: We identified both detrimental and beneficial associations of social network parameters with the prevalence of infections. Our findings can be used to complement mathematical models on infection spread, as well as to optimize current infectious disease control.
Original languageEnglish
Article number300
JournalBMC Infectious Diseases
Issue number1
Publication statusPublished - 2018

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