Reduced Bias for respondent driven sampling: accounting for non-uniform edge sampling probabilities in people who inject drugs in Mauritius

TL;DR

This paper introduces a new method to improve respondent driven sampling estimates by accounting for non-uniform edge sampling probabilities, enhancing accuracy in hard-to-reach populations like people who inject drugs.

Contribution

It develops a novel approach for adjusting RDS estimators using estimated edge inclusion probabilities to correct bias caused by unequal sampling chances.

Findings

Improved HIV and Hepatitis C prevalence estimates in Mauritius.

Demonstrated bias sensitivity of existing RDS estimators.

Validated the new method with real-world data.

Abstract

People who inject drugs are an important population to study in order to reduce transmission of blood-borne illnesses including HIV and Hepatitis. In this paper we estimate the HIV and Hepatitis C prevalence among people who inject drugs, as well as the proportion of people who inject drugs who are female in Mauritius. Respondent driven sampling (RDS), a widely adopted link-tracing sampling design used to collect samples from hard-to-reach human populations, was used to collect this sample. The random walk approximation underlying many common RDS estimators assumes that each social relation (edge) in the underlying social network has an equal probability of being traced in the collection of the sample. This assumption does not hold in practice. We show that certain RDS estimators are sensitive to the violation of this assumption. In order to address this limitation in current methodology, and the impact it may have on prevalence estimates, we present a new method for improving RDS prevalence estimators using estimated edge inclusion probabilities, and apply this to data from Mauritius.