Workshop: Reaching out to engaging the risk groups: online respondent-driven methods for public health
Authors:   Organised by: Netherlands National Institute for Public Health and the Environment Chair persons: Aura Timen - EUPHA (IDC), Anna Thorson - Sweden
Published: November 2019

Abstract

Hard-to-reach populations (i.e. those stigmatised, marginalised, underrepresented, or otherwise disadvantaged) such as men who have sex with men and immigrants are at higher risk for infectious diseases. Reaching these populations, studying their behaviour and/or testing individuals for infectious diseases is essential for developing effective prevention programmes and disease surveillance. These populations, however, lack sampling frames making it difficult to collect representative quantitative data using common probability-based sampling methods. Respondent-driven sampling (RDS), a variant of snowball sampling, is an effective method to recruit these populations and to make unbiased population estimates using a statistical model. RDS starts with recruiting a convenience sample of the target population (so-called “seeds”). These seeds are then asked to recruit a number of other eligible individuals of their social network. This process continues which leads to chains of recruitment, with several waves of recruits.
The process of respondent-driven recruitment is very similar to the way infectious diseases such as influenza and mumps transmit through populations. This provides opportunities to use the method with a different aim: the detection of cases within networks. Finding infectious cases is an essential element for prevention of further spread in the population and individual health consequences. Essential as it is to public health, conventional contact tracing is a rather timely, costly and, up to a certain degree, really frustrating activity. Studying and making use of social networks may help to understand and control the spread of infectious diseases transmitted via direct contact. These diseases do not spread at random through a population, but follow the underlying patterns of contact networks. This entails that cases tend to cluster by time and space, and their contact persons are at a higher risk for infection. Same as with RDS, respondent-driven detection (RDD) starts with individuals being asked to recruit relevant contact persons from their network. These contact persons are then asked to do the same, resulting in successive waves of contact persons. A case is reached through contact with a known case, similar to pathogens spreading through these contact relationships. RDD may therefore enhance conventional contact tracing, providing further insight in the extent of outbreaks, in a quick and less laborious manner for public health professionals.
Using three examples from public health practice, this workshop provides participants insights in the methodology of online respondent-driven methods (RDS and RDD), how these provide behavioural and epidemiological knowledge on networks and the spread of infectious diseases, and highlights pre-requisites for successful implementation in practice. Lastly, an interactive discussion will be held with the audience on how attendees can apply these methods for their own public health challenges.

Key messages
RDS is used to sample hard-to-reach populations to collect their social, sexual and behavioural information, and to make unbiased population estimates. RDD is used to detect infectious cases or clusters of disease.