Incorporating respondent-driven sampling into web-based discrete choice experiments: preferences for COVID-19...

Area of Science:

• Public Health
• Behavioral Economics
• Epidemiology

Background:

• Nonpharmaceutical interventions (NPIs) like social distancing were crucial for controlling COVID-19 spread.
• Understanding public preferences for NPIs is vital for effective policy-making.
• Traditional sampling methods often struggle to reach diverse populations for such studies.

Purpose of the Study:

• To pilot a web-based Respondent Driven Sampling (RDS) approach combined with a Discrete Choice Experiment (DCE) to assess individual preferences for COVID-19 mitigation strategies.
• To evaluate the feasibility and potential of RDS and DCE for scalable, generalizable public health preference research.
• To identify key factors influencing participant choices regarding NPIs.

Main Methods:

• A web-based Discrete Choice Experiment (DCE) was administered to participants recruited via Respondent Driven Sampling (RDS) across three countries.
• A rank-ordered mixed logit model was employed to analyze the choice data.
• The study involved university students and staff, with a focus on engagement and response rates.

Main Results:

• Participants prioritized mitigation strategies that minimized COVID-19 risk, such as increased sheltering-in-place.
• Financial compensation from the government was a significant factor in preference.
• Minimizing personal health (mental and physical) and financial problems were also key preferences.

Conclusions:

• Web-based RDS and DCE are effective tools for recruiting participants and gathering data on public health preferences.
• The methodology shows potential for scale-up to achieve nationally-representative estimates.
• Findings highlight the need for policies that balance public health goals with economic and personal well-being.