The Global Burden of Disease (GBD) remains the most widely accepted metric in quantifying health burdens of disease states and risk factors globally. Studies have attempted to quantify the GBD associated with work-related exposures and conditions, but none have focused on migrant workers, who are known to experience higher risks of work-related injuries and deaths, and diverse forms of abuse and exploitation. Moreover, few studies have critically reflected on the GBD methodology from an interdisciplinary perspective, or explored expanding the GBD methods to account for non-disease-related risk factors. This qualitative study presents findings from interviews with 11 experts with diverse disciplinary backgrounds from academia or UN agencies, many of whom have produced burden of disease estimates, to identify data and methodological challenges, evidence needs, and potential uses for new GBD estimates on labour exploitation in migrant populations. The most common challenges included difficulty of defining and operationalizing the exposure “labour exploitation” in migrant populations, scarcity of datasets with migrants’ work and health data, challenges gathering data among vulnerable migrants, and difficulty establishing causality to obtain attributable fractions required in GBD estimations. Despite these challenges, experts agreed that such global estimates would be valuable in quantifying the social and economic costs of labour exploitation among migrants, which can be used to advance migrants’ right to a safe and healthy working environment. Nevertheless, experts cautioned that such global estimates could easily be misused or misinterpreted by end users, thus it is important to make clear the assumptions and limitations when presenting findings. • The Global Burden of Disease (GBD) of labour exploitation in migrants is unknown. • Experts agree on the value of GBD for quantifying the health impacts of labour exploitation. • GBD estimates can advance safe and healthy work and employment conditions. • GBD challenges include defining exploitation, data scarcity, and proving causality. • GBD assumptions and limitations must be clear to avoid misuse or misinterpretation.
Lau et al. (Fri,) studied this question.