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

Navigating Public Policy Responses to a Pandemic: The Balancing Act Between Physical Health, Mental Health, and Household Income

OBJECTIVE

During COVID-19, governments imposed restrictions that reduced pandemic-related health risks but likely increased personal and societal mental health risk, partly through reductions in household income. This study aimed to quantify the public's willingness to accept trade-offs between pandemic health risks, household income reduction, and increased risk of mental illness that may result from future pandemic-related policies.

METHODS

A total of 547 adults from an online panel participated in a discrete choice experiment where they were asked to choose between hypothetical future pandemic scenarios. Each scenario was characterized by personal and societal risks of dying from the pandemic, experiencing long-term complications, developing anxiety/depression, and reductions in household income. A latent class regression was used to estimate trade-offs.

RESULTS

Respondents state a willingness to make trade-offs across these attributes if the benefits are large enough. They are willing to accept 0.8% lower household income (0.7-1.0), 2.7% higher personal risk of anxiety/depression (1.8-3.6), or 3.2% higher societal rate of anxiety/depression (1.7-4.7) in exchange for 300 fewer deaths from the pandemic.

CONCLUSION

Results reveal that individuals are willing to accept lower household income and higher rates of mental illness, both personal and societal, if the physical health benefits are large enough. Respondents placed greater emphasis on maintaining personal, as opposed to societal, mental health risk and were most interested in preventing pandemic-related deaths. Governments should consider less restrictive policies when pandemics have high morbidity but low mortality to avoid the prospect of improving physical health while simultaneously reducing net social welfare.

Employing Respondent Driven Sampling (RDS) to recruit people who inject drugs (PWID) and other hard-to-reach populations during COVID-19: Lessons learned

BACKGROUND

Respondent Driven Sampling (RDS) is an effective sampling strategy to recruit hard-to-reach populations but the impact of the COVID-19 pandemic on the use of this strategy in the collection of data involving human subjects, particularly among marginalized and vulnerable populations, is not known. Based on an ongoing study using RDS to recruit and study the interactions between HIV infection, injection drug use, and the microbiome in Puerto Rico, this paper explores the effectiveness of RDS during the pandemic and provided potential strategies that could improve recruitment and data collection.

RESULTS

RDS was employed to evaluate its effectiveness in recruiting a group of people who inject drugs (PWID) and controls (N = 127) into a study in the midst of the COVID-19 pandemic. The participants were distributed among three subsets: 15 were HIV+ and PWID, 58 were HIV- PWID, and 54 were HIV+ and not PWID.

FINDINGS

Results show that recruitment through peer networks using RDS was possible across all sub-groups. Yet, while those in the HIV+ PWID sub-group managed to recruit from other-sub groups of HIV- PWID and HIV+, this occurred at a lower frequency.

CONCLUSION

Despite the barriers introduced by COVID-19, it is clear that even in this environment, RDS continues to play a powerful role in recruiting hard-to-reach populations. Yet, more attention should be paid at how future pandemics, natural disasters, and other big events might affect RDS recruitment of vulnerable and hard-to-reach populations.