摘要：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. Syndromic surveillance provides information necessary to monitor trends in disease incidence and implement and evaluate response plans. 1,2 To date, most efforts have focused on developing systems based on data from inpatient and ambulatory care health records. 3 In a majority of high-income countries, including the Netherlands, influenza surveillance is based on a combination of reports of influenza-like illness (ILI) collected by sentinel surveillance clinics and additional microbiological testing of subgroups of symptomatic patients. 4 This type of system excludes symptomatic patients who do not visit a general practitioner, and such patients are likely to account for the majority of cases in most influenza outbreaks. 5 Many communicable diseases (e.g., influenza, severe acute respiratory syndrome, measles) spread largely between socially connected individuals, such as household members and schoolchildren, and they often occur in clusters. 6,7 Therefore, cases of infection are expected to cluster in social networks (i.e., contacts of an infected individual are infected at a level of probability higher than that expected if the distribution was random), and clusters can be detected via local social networks of individuals reporting symptoms. Increased Internet use facilitated the emergence of participatory surveillance (PS) systems, which enable real-time monitoring of diseases through regular submission of syndromic information by volunteers. 8,9 These systems provide information that is not collected in regular surveillance, such as the proportion of symptomatic individuals who actually visit a general practitioner and the proportion who are hospitalized. To test the feasibility of eliciting information about infections in local networks of symptomatic individuals, we combined a chain recruitment method with existing online PS platforms. Under certain conditions, such a recruitment method permits stepwise and controlled sampling of contacts of contacts, and so forth, in social networks in the general population. 10 We asked PS volunteers to complete a questionnaire and to invite their contacts into the study. In this way, we collected data on chains of contacts to analyze whether other symptomatic individuals could be detected via the local social network of symptomatic respondents. Our aims were to determine whether respondents can be recruited via respondent-driven detection, to report on which individuals are reached, and to assess whether there is clustering of symptomatic patients.
