Health surveillance through social networks

Abstract

We propose a network-based method to monitor health behaviors and point out the general conditions for it to work effectively. The method helps to identify effective informants for monitoring future health behaviors and to triangulate self-reports of sensitive health behaviors. We demonstrate the method by studying the smoking behaviors of over 4000 middle school students in China. Using students’ observations of their schoolmates smoking in the past 30 days, we construct smoking detection networks and examine the patterns of smoking detection. We find that smokers, optimistic students, and popular students make better informants than their counterparts. We also find that using three to four (or the 3rd quartile of) positive peer reports can uncover a good number of under-reported smokers while not producing excessive false positives.