A Correlated Network Scale-up Model: Finding the Connection Between Subpopulations

TL;DR

This paper introduces a new correlated network scale-up model that improves estimation of hidden population sizes and network features using aggregated relational data, aiding public health efforts.

Contribution

It proposes a novel ARD model incorporating covariates and correlation structures, enhancing bias reduction and understanding of network connections without full network data.

Findings

Improved size estimates for HIV-related groups in Ukraine.

Better understanding of factors influencing social connections.

Enhanced methods for recruiting marginalized populations.

Abstract

Aggregated relational data (ARD), formed from "How many X's do you know?" questions, is a powerful tool for learning important network characteristics with incomplete network data. Compared to traditional survey methods, ARD is attractive as it does not require a sample from the target population and does not ask respondents to self-reveal their own status. This is helpful for studying hard-to-reach populations like female sex workers who may be hesitant to reveal their status. From December 2008 to February 2009, the Kiev International Institute of Sociology (KIIS) collected ARD from 10,866 respondents to estimate the size of HIV-related groups in Ukraine. To analyze this data, we propose a new ARD model which incorporates respondent and group covariates in a regression framework and includes a bias term that is correlated between groups. We also introduce a new scaling procedure utilizing the correlation structure to further reduce biases. The resulting size estimates of those most-at-risk of HIV infection can improve the HIV response efficiency in Ukraine. Additionally, the proposed model allows us to better understand two network features without the full network data: 1. What characteristics affect who respondents know, and 2. How is knowing someone from one group related to knowing people from other groups. These features can allow researchers to better recruit marginalized individuals into the prevention and treatment programs. Our proposed model and several existing NSUM models are implemented in the networkscaleup R package.