This study aims to assess the feasibility of respondent-driven sampling (RDS) to recruit participants with recent abortion experiences in humanitarian contexts, and describe the composition of the study sample generated with this sampling method.

This was a three-phase mixed-methods community-engaged research study employing an exploratory and explanatory sequential approach. We conducted in-depth interviews, focus group discussions, an interviewer-administered questionnaire on abortion experiences and a health facility assessment.

Bidibidi Refugee Settlement, Uganda and Kakuma Refugee Camp, Kenya from November 2021 to December 2022.

Using RDS, we recruited 600 participants in Kakuma and 601 participants in Bidibidi with recent abortion experiences. In Kakuma, participants were primarily from Burundi, the Democratic Republic of the Congo and South Sudan; participants in Bidibidi were primarily from South Sudan. Most participants in both sites had completed at least some primary school and were not employed.

RDS recruitment dynamics: convergence and bottlenecks on key sociodemographic variables, recruitment and population homophily, reciprocity of social ties, success and experiences recruiting.

There were minor violations of RDS assumptions, particularly regarding assumptions of reciprocity of ties and seed composition independent of sample. In addition, there was a strong tendency of participants to recruit those from the same home country and living within the same camp zone. However, sample proportions for age, home country, marital status, zone of residence and student status reached equilibrium (stabilised) by around 500 participants at each site, and we were able to quickly attain the study sample size.

While the true representativeness of our sample remains unknown, RDS is a practical and effective recruitment method in humanitarian contexts for sensitive topics, particularly for research questions in which no data or sampling frames exist. However, attention to representativeness and community engagement is essential to optimising its application and ensuring success.

This study aims to assess the economic feasibility and broader policy implications of the Korea International Cooperation Agency’s (KOICA) official development assistance (ODA) projects for the COVID-19 emergency response in Uzbekistan through a cost–benefit analysis. The primary research question is to evaluate whether the interventions provide sufficient economic returns relative to their costs while informing future pandemic preparedness of response strategies.

A cost–benefit analysis using quantitative methods was performed to assess the financial impact of the COVID-19 ODA projects.

The study was conducted in Uzbekistan, focusing on KOICA’s COVID-19 emergency response projects from January 2020 to December 2021.

The intervention involved the implementation of KOICA’s COVID-19 emergency response activities, including early diagnosis support, establishment of rapid response bases, provision of medical equipment and emergency relief efforts in Uzbekistan.

The primary outcome measure is the net present value (NPV) and benefit/cost ratio of the project. Secondary outcome measures are the project’s effectiveness in terms of death prevention, reduction in medical costs and timely COVID-19 testing.

The analysis revealed a total programme cost of US$11 353 173, with a net benefit ranging from US$21 026 032 to US$34 573 403, and a benefit/cost ratio between 1.85 and 3.05. A detailed examination of costs and benefits highlighted the programme’s positive NPV and benefit/cost ratio, indicating its economic feasibility. The study also underscored the programme’s effectiveness in preventing deaths, reducing medical costs and providing timely COVID-19 testing.

The findings confirm that KOICA’s COVID-19 emergency response projects in Uzbekistan were economically viable and effective. The study highlights the importance of integrating economic evaluations into ODA assessments, particularly for emergency response and infectious disease control. It recommends expanding the use of quantitative analysis to optimise resource allocation and improve decision-making in future global health crises.