Public preferences for interventions to prevent emerging infectious disease threats: a discrete choice experiment

Objective

When faced with an emergent epidemic with high mortality and morbidity potential, policy makers must decide what public health interventions to deploy at different stages of the outbreak. However, almost nothing is known about how the public view these interventions or how they trade off risks (of disease) with inconvenience (of interventions). In this paper, we aim to understand public perceptions on pandemic interventions, as well as to identify if there are any distinct respondent preference classes.

Design

A discrete choice experiment.

Setting

This study was fielded in Singapore between November 2012 and February 2013.

Participants

A random sample of 500 Singapore residents aged 21 and over, including 271 women and 229 men, was analysed.

Outcome measures

Demographic information was collected from each participant. Participants were also shown a series of pairs of alternatives, each combining interventions and morbidity, mortality and cost outcomes and declared a preference for one combination. A random utility model was developed to determine the individual's preference for interventions and a hierarchical cluster analysis was performed to identify distinct respondent preference classes.

Results

On average, participants preferred more intense interventions, and preferred scenarios with fewer deaths and lower tax. The number of infections did not significantly influence respondents' responses. We identified two broad classes of respondents: those who were mortality averse and those who were expenditure averse. Education was found to be a predictor of group membership.

Conclusion

Overall, there was considerable support for government interventions to prevent or mitigate outbreaks of emerging infectious diseases, including those that greatly restricted individual liberties, as long as the restrictions showed a reasonable chance of reducing the adverse health effects of the outbreak.

http://ift.tt/2oarNQp