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

Online respondent-driven sampling for studying contact patterns relevant for the spread of close-contact pathogens: a pilot study in Thailand
Online respondent-driven sampling for studying contact patterns relevant for the spread of close-contact pathogens: a pilot study in Thailand
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.
1932-6203
1-11
Stein, Mart L.
6c39b550-56f1-4515-b85d-367d87ca31bb
van Steenbergen, Jim E.
99c4f75d-40c1-4aec-bc78-2e024e0369f2
Chanyasanha, Charnchudhi
85ffe35a-cf88-448b-a195-7c81d2c1dd8f
Tipayamongkholgul, Mathuros
134f890a-0b68-4af1-91cd-4c6608cd3641
Buskens, Vincent
45f4c94a-b1c3-4a9c-8c54-546d07d6666c
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Sabaiwan, Wasamon
f195cd1b-1589-4704-a971-f77b4fc1dd0e
Bengtsson, Linus
f7585eb4-9e78-422d-8178-4310985aa24e
Lu, Xin
a681bac0-d6d1-4e8e-a642-4ce42ae2cc9d
Thorson, Anna E.
ce1e1024-6169-473a-828b-6d38d9107b05
Kretzschmar, Mirjam E.E.
acee7a3c-75ce-43fc-b630-a7ad06121292
Stein, Mart L.
6c39b550-56f1-4515-b85d-367d87ca31bb
van Steenbergen, Jim E.
99c4f75d-40c1-4aec-bc78-2e024e0369f2
Chanyasanha, Charnchudhi
85ffe35a-cf88-448b-a195-7c81d2c1dd8f
Tipayamongkholgul, Mathuros
134f890a-0b68-4af1-91cd-4c6608cd3641
Buskens, Vincent
45f4c94a-b1c3-4a9c-8c54-546d07d6666c
van der Heijden, Peter G.M.
85157917-3b33-4683-81be-713f987fd612
Sabaiwan, Wasamon
f195cd1b-1589-4704-a971-f77b4fc1dd0e
Bengtsson, Linus
f7585eb4-9e78-422d-8178-4310985aa24e
Lu, Xin
a681bac0-d6d1-4e8e-a642-4ce42ae2cc9d
Thorson, Anna E.
ce1e1024-6169-473a-828b-6d38d9107b05
Kretzschmar, Mirjam E.E.
acee7a3c-75ce-43fc-b630-a7ad06121292

Stein, Mart L., van Steenbergen, Jim E., Chanyasanha, Charnchudhi, Tipayamongkholgul, Mathuros, Buskens, Vincent, van der Heijden, Peter G.M., Sabaiwan, Wasamon, Bengtsson, Linus, Lu, Xin, Thorson, Anna E. and Kretzschmar, Mirjam E.E. (2014) Online respondent-driven sampling for studying contact patterns relevant for the spread of close-contact pathogens: a pilot study in Thailand. PLoS ONE, 9 (1), 1-11, [e85256]. (doi:10.1371/journal.pone.0085256). (PMID:24416371)

Record type: Article

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.

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Published date: 8 January 2014
Organisations: Social Statistics & Demography

Identifiers

Local EPrints ID: 369725
URI: http://eprints.soton.ac.uk/id/eprint/369725
ISSN: 1932-6203
PURE UUID: 735228b5-7293-4ce1-82e5-29cb57807275
ORCID for Peter G.M. van der Heijden: ORCID iD orcid.org/0000-0002-3345-096X

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Date deposited: 06 Oct 2014 11:41
Last modified: 15 Mar 2024 03:46

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Contributors

Author: Mart L. Stein
Author: Jim E. van Steenbergen
Author: Charnchudhi Chanyasanha
Author: Mathuros Tipayamongkholgul
Author: Vincent Buskens
Author: Wasamon Sabaiwan
Author: Linus Bengtsson
Author: Xin Lu
Author: Anna E. Thorson
Author: Mirjam E.E. Kretzschmar

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