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. Existing studies on these migrant domestic workers are mostly short-term surveys; no one has tracked their health over time or tested interventions. This project aims to fill that gap. The researchers have already developed a package of support—training, peer-support groups, and mobile apps—and now plan to build a risk-stratification tool that identifies which women are most vulnerable, then pilot and evaluate the intervention’s impact and cost-effectiveness. If successful, this could give governments and recruitment agencies a practical, scalable way to protect hundreds of thousands of women before and during migration. It would shift the response from reactive crisis management to proactive, data-driven healthcare support—improving a system that currently leaves these workers largely invisible to health services.
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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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