Associated organisationsAddis Ababa University · Armauer Hansen Research Institute · St. Paul's Hospital Millennium Medical CollegeEurope PMC affiliations are not treated as award recipients or mapped locations.
Funding£810K
PeriodNov 2025 — Oct 2029
In plain English
AI plain-English summary
Every year, more than 350,000 Ethiopian women leave for domestic work in the Middle East, where they face overwork, mental and sexual health risks, and limited access to healthcare. This matters because existing studies on migrant domestic workers are mostly short-term snapshots. No one has tested whether a long-term, tailored health intervention can actually improve outcomes for this vulnerable group. The researchers plan to fill that gap by combining a risk-stratification tool—built from pre-migration data and data science—with a package of training, peer-support groups, and mobile health apps. If the intervention proves effective and cost-effective, it could change how governments and recruitment agencies support these women before and after they migrate. Rather than a one-size-fits-all medical check-up, workers could receive support matched to their specific risks. That would mean fewer women suffering in silence from untreated mental health conditions, sexual health problems, or the effects of overwork—a quiet but profound improvement to a system that currently leaves many without care.
View original technical description
Every year, more than 350,000 Ethiopian women migrate to the Middle East legally, and many others illegally, seeking domestic work. Economic crisis, rising unemployment, and internal conflict contribute to this massive migration. The bilateral agreement between the Ethiopian government and each destination country requires recruitment agencies to undertake rigorous pre- migration medical check-up for migrant domestic workers (MDWs). Nevertheless, MDWs in the Middle East are subject to overwork; mental, sexual and reproductive health challenges; and limited healthcare access. Studies on MDWs are mostly exploratory or cross-sectional; longitudinal and intervention studies are lacking. We propose to provide healthcare support to these vulnerable women by leveraging social networks, social media, and innovative technology. In Phase I, we explored key health challenges of MDWs through a scoping review, stakeholder consultation, and analysis of pre-migration data. We hypothesized that, by utilizing epidemiological and data science intelligence methods, we could stratify MDWs based on their level of risk and provide tailored intervention. We developed an intervention package, including training, peer-support and self-help groups, and mobile applications. In Phase II, we propose to i) develop, refine and validate a risk stratification tool, ii) refine and pilot the intervention and iii) implement and evaluate its impact and cost-effectiveness.
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