Improving myalgic encephalomyelitis population sampling: Applying an online respondent-driven method to address biases in G93.3 register data

Abstract

With widespread late- and under-diagnosing, health register code G93.3 data cannot offer an unbiased sampling frame for myalgic encephalomyelitis, complicating prevalence and demographic distribution assessments. It also remains unclear if all G93.3 cases would meet the Canada Consensus Criteria (CCC). This article describes a novel methodological approach to addressing selection bias when estimating a CCC population's characteristics, applying an online respondent-driven sampling approach and validated DePaul University algorithms. In a sample of 660 respondents, we assess possible bias in the G93.3 diagnosis by regressing sociodemographic factors on G93.3 status, controlling for medical factors. Results support suggestions that G93.3 register data are biased against those socially deprived.