Survey of risk perception, trust, and behavioral intention during the COVID-19 pandemic

Early public health strategies to prevent the spread of COVID-19 in the
United States relied on non-pharmaceutical interventions (NPIs) as
vaccines and therapeutic treatments were not yet available. Implementation
of NPIs, primarily social distancing and mask wearing, varied widely
between communities within the US due to variable government mandates, as
well as differences in attitudes and opinions. To understand the interplay
of trust, risk perception, behavioral intention, and disease burden, we
developed a survey instrument to study attitudes concerning COVID-19 and
pandemic behavioral change in three states: Idaho, Texas, and Vermont. We
designed our survey (n = 1034) to detect whether these
relationships were significantly different in rural populations. The best
fitting structural equation models show that trust indirectly affects
protective pandemic behaviors via health and economic risk perception. We
explore two different variations of this social cognitive model: the first
assumes behavioral intention affects future disease burden while the
second assumes that observed disease burden affects behavioral intention.
In our models we include several exogenous variables to control for
demographic and geographic effects. Notably, political ideology is the
only exogenous variable which significantly affects all aspects of the
social cognitive model (trust, risk perception, and behavioral intention).
While there is a direct negative effect associated with rurality on
disease burden, likely due to the protective effect of low population
density in the early pandemic waves, we found a marginally significant,
positive, indirect effect of rurality on disease burden via decreased
trust (p = 0.095). This trust deficit creates additional vulnerabilities
to COVID-19 in rural communities which also have reduced healthcare
capacity. Increasing trust by methods such as in-group messaging could
potentially remove some of the disparities inferred by our models and
increase NPI effectiveness.