Estimating the Size of Key Affected Populations in Concentrated HIV/AIDS Epidemics Using the Network Scale up Method

Rachael Maltiel, Expedia, Inc.
Adrian Raftery, University of Washington

We develop methods for estimating hard-to-reach populations from data collected using network-based questions on standard surveys. Such data arise by asking respondents how many people they know in a specific group (e.g. people named Michael, intravenous drug users). The Network Scale up Method (NSUM) is a tool for producing population size estimates using these indirect measures. We extend previously proposed estimators by treating personal network sizes as random effects, yielding principled statements of uncertainty. This allows us to generalize the model to account for variation in people’s propensity to know people in particular subgroups (barrier effects), as well as their lack of awareness of or reluctance to acknowledge their contacts’ group memberships (transmission bias). We apply our methods to data from a study of HIV/AIDS prevalence in Curitiba, Brazil. Our results show that when transmission bias is present, external information about its likely extent can greatly improve the estimates.