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PLoS One. 2014 Jan 8;9(1):e85256. doi: 10.1371/journal.pone.0085256. eCollection 2014.

Online respondent-driven sampling for studying contact patterns relevant for the spread of close-contact pathogens: a pilot study in Thailand.

Author information

1
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands ; Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands.
2
Centre for Infectious Disease Control, National Institute for Public Health and the Environment, Bilthoven, The Netherlands ; Centre for Infectious Diseases, Leiden University Medical Centre, Leiden, The Netherlands.
3
Department of Microbiology, Faculty of Public Health, Mahidol University, Bangkok, Thailand.
4
Department of Epidemiology, Faculty of Public Health, Mahidol University, Bangkok, Thailand.
5
Faculty of Social and Behavioural Sciences, University Utrecht, Utrecht, The Netherlands.
6
Faculty of Social and Behavioural Sciences, University Utrecht, Utrecht, The Netherlands ; Southampton Statistical Sciences Research Institute, University of Southampton, Southampton, United Kingdom.
7
Faculty of Communication Arts, Chulalongkorn University, Bangkok, Thailand.
8
Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden.
9
Department of Public Health Sciences, Karolinska Institutet, Stockholm, Sweden ; College of Information System and Management, National University of Defense Technology, Changsha, China.

Abstract

BACKGROUND:

Information on social interactions is needed to understand the spread of airborne infections through a population. Previous studies mostly collected egocentric information of independent respondents with self-reported information about contacts. Respondent-driven sampling (RDS) is a sampling technique allowing respondents to recruit contacts from their social network. We explored the feasibility of webRDS for studying contact patterns relevant for the spread of respiratory pathogens.

MATERIALS AND METHODS:

We developed a webRDS system for facilitating and tracking recruitment by Facebook and email. One-day diary surveys were conducted by applying webRDS among a convenience sample of Thai students. Students were asked to record numbers of contacts at different settings and self-reported influenza-like-illness symptoms, and to recruit four contacts whom they had met in the previous week. Contacts were asked to do the same to create a network tree of socially connected individuals. Correlations between linked individuals were analysed to investigate assortativity within networks.

RESULTS:

We reached up to 6 waves of contacts of initial respondents, using only non-material incentives. Forty-four (23.0%) of the initially approached students recruited one or more contacts. In total 257 persons participated, of which 168 (65.4%) were recruited by others. Facebook was the most popular recruitment option (45.1%). Strong assortative mixing was seen by age, gender and education, indicating a tendency of respondents to connect to contacts with similar characteristics. Random mixing was seen by reported number of daily contacts.

CONCLUSIONS:

Despite methodological challenges (e.g. clustering among respondents and their contacts), applying RDS provides new insights in mixing patterns relevant for close-contact infections in real-world networks. Such information increases our knowledge of the transmission of respiratory infections within populations and can be used to improve existing modelling approaches. It is worthwhile to further develop and explore webRDS for the detection of clusters of respiratory symptoms in social networks.

PMID:
24416371
PMCID:
PMC3885693
DOI:
10.1371/journal.pone.0085256
[Indexed for MEDLINE]
Free PMC Article

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