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People who inject drugs are an important population to study in order toreduce transmission of blood-borne illnesses including HIV and Hepatitis. Inthis paper we estimate the HIV and Hepatitis C prevalence among people whoinject drugs, as well as the proportion of people who inject drugs who arefemale in Mauritius. Respondent driven sampling (RDS), a widely adoptedlink-tracing sampling design used to collect samples from hard-to-reach humanpopulations, was used to collect this sample. The random walk approximationunderlying many common RDS estimators assumes that each social relation (edge)in the underlying social network has an equal probability of being traced inthe collection of the sample. This assumption does not hold in practice. Weshow that certain RDS estimators are sensitive to the violation of thisassumption. In order to address this limitation in current methodology, and theimpact it may have on prevalence estimates, we present a new method forimproving RDS prevalence estimators using estimated edge inclusionprobabilities, and apply this to data from Mauritius.

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