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Enhancing syndromic surveillance with online respondent-driven detection

Author

Listed:
  • Stein, M.L.
  • Van Steenbergen, J.E.
  • Buskens, V.
  • Van Der Heijden, P.G.M.
  • Koppeschaar, C.E.
  • Bengtsson, L.
  • Thorson, A.
  • Kretzschmar, M.E.E.

Abstract

Objectives. We investigated the feasibility of combining an online chain recruitment method (respondent-driven detection) and participatory surveillance panels to collect previously undetected information on infectious diseases via social networks of participants. Methods. In 2014, volunteers from 2 large panels in the Netherlands were invited to complete a survey focusing on symptoms of upper respiratory tract infections and to invite 4 individuals they had met in the preceding 2 weeks to take part in the study. We compared sociodemographic characteristics among panel participants, individuals who volunteered for our survey, and individuals recruited via respondent-driven detection. Results. Starting from 1015 panel members, the survey spread through all provinces of the Netherlands and all age groups in 83 days. A total of 433 individuals completed the survey via peer recruitment. Participants who reported symptoms were 6.1% (95% confidence interval = 5.4, 6.9) more likely to invite contact persons than were participants who did not report symptoms. Participants with symptoms invited more symptomatic recruits to take part than did participants without symptoms. Conclusions. Our findings suggest that online respondent-driven detection can enhance identification of symptomatic patients by making use of individuals' local social networks. © 2015, American Public Health Association Inc. All rights reserved.

Suggested Citation

  • Stein, M.L. & Van Steenbergen, J.E. & Buskens, V. & Van Der Heijden, P.G.M. & Koppeschaar, C.E. & Bengtsson, L. & Thorson, A. & Kretzschmar, M.E.E., 2015. "Enhancing syndromic surveillance with online respondent-driven detection," American Journal of Public Health, American Public Health Association, vol. 105(8), pages 90-97.
    • Handle: RePEc:aph:ajpbhl:10.2105/ajph.2015.302717_0
    DOI: 10.2105/AJPH.2015.302717
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    Cited by:

    1. Mart L Stein & Vincent Buskens & Peter G M van der Heijden & Jim E van Steenbergen & Albert Wong & Martin C J Bootsma & Mirjam E E Kretzschmar, 2018. "A stochastic simulation model to study respondent-driven recruitment," PLOS ONE, Public Library of Science, vol. 13(11), pages 1-19, November.

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