Estimating population size using the network scale up method

Abstract

We develop methods for estimating the size of 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 of respondents’ networks. Killworth et al. (1998a,b) proposed maximum likelihood estimators of population size for a fixed effects model in which respondents’ degrees or personal network sizes are treated as fixed. We extend this 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), such as their tendency to know people like themselves, as well as their lack of awareness of or reluctance to acknowledge their contacts’ group memberships (transmission bias). NSUM estimates also suffer from recall bias, in which respondents tend to underestimate the number of members of larger groups that they know, and conversely for smaller groups. We propose a data-driven adjustment method to deal with this. Our methods perform well in simulation studies, generating improved estimates and calibrated uncertainty intervals, as well as in back estimates of real sample data. We apply them 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. The methods are implemented in the NSUM R package.

Population size estimation of female sex workers in Iran: Synthesis of methods and results

“…Given the absence of a single gold standard or bias-free approach, we adapted and applied several methods, namely wisdom of the crowds (WOTC) [ 7 , 8 ], service and unique object multipliers [ 1 , 3 , 9 ], and network scale-up (NSU) [ 10 , 11 ]), to estimate the population size of FSW in Iran. We synthesized the findings of these several approaches to arrive at a more robust estimate to better inform health policies and guide research to improve the health of FSW in Iran.…”

Mulheres que fizeram aborto no Município do Rio de Janeiro, Brasil: aplicação de um modelo hierárquico bayesiano

“…Although this result suggests that the proposed model underestimates the uncertainty, the result corroborates those reported by Salganik et al 16 . According to Maltiel et al 30 , by using external information 30 , the network scale-up method can improve the estimates and their associated uncertainty. We thus suggest that in the future, the sources of uncertainty should be captured in order to minimize the methodology's intrinsic biases.…”

Estimating Religious Populations with the Network Scale‐Up Method: A Practical Alternative to Self‐Report

“…Some scholars, particularly McCormick and others (Maltiel et al. ; McCormick, Salganik, and Zheng ), used a model‐based approach to treat the biases as arising from an underlying binomial distribution, such that the barrier bias is subject to a probability process and the transmission bias is subject to a probability process . Zheng, Salganik, and Gelman () modeled the biases with an overdispersed Poisson model with the similar model‐based ideas.…”