Hidden Population Size Estimation From Respondent-Driven Sampling: A Network Approach. Issue 522 (3rd April 2018)

• Record Type: Journal Article
• Authors: Crawford, Forrest W.; Wu, Jiacheng; Heimer, Robert
• Is Part Of: Journal of the American Statistical Association. Volume 113:Issue 522(2018)
• Page Start: 755
• Page End: 766
• Publication Date: 2018-04-03
• Subjects: Hidden population -- Injection drug use -- Network inference -- Population size; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2017.1285775
• Languages: English
• ISSNs: 0162-1459

Abstract

ABSTRACT: Estimating the size of stigmatized, hidden, or hard-to-reach populations is a major problem in epidemiology, demography, and public health research. Capture–recapture and multiplier methods are standard tools for inference of hidden population sizes, but they require random sampling of target population members, which is rarely possible. Respondent-driven sampling (RDS) is a survey method for hidden populations that relies on social link tracing. The RDS recruitment process is designed to spread through the social network connecting members of the target population. In this article, we show how to use network data revealed by RDS to estimate hidden population size. The key insight is that the recruitment chain, timing of recruitments, and network degrees of recruited subjects provide information about the number of individuals belonging to the target population who are not yet in the sample. We use a computationally efficient Bayesian method to integrate over the missing edges in the subgraph of recruited individuals. We validate the method using simulated data and apply the technique to estimate the number of people who inject drugs in St. Petersburg, Russia. Supplementary materials for this article are available online.