Ata Aydin Uslu

Compartmental models of infectious disease transmission make assumptions about human behaviors. Specifically, they parameterize interactions across population groups, assumed to have distinct epidemiologically relevant behavioral patterns, primarily through contact matrices stratified by demographic variables such as age, gender, or socioeconomic status. Although such demographic characteristics are readily measurable, they may inadequately capture the social and psychological forces that govern protective behaviors. Drawing on 20 waves of a national survey conducted throughout the COVID-19 pandemic in the United States, we show that institutional trust—particularly trust in public health agencies, physicians, and hospitals—is a dominant predictor of protective behavior adoption. For mask wearing during periods of strongest pandemic activity, institutional trust explains more behavioral difference across population groups than age, income, education, and partisan affiliation combined. In unadjusted analyses, the difference in protective behavior adoption between individuals with the highest and lowest trust in the US Centers for Disease Control and Prevention (CDC) was four- to six-fold larger than the corresponding differences by age, income, or education, and exceeded the difference between Democratic and Republican respondents. This association was institutionally specific (the relationship attenuates for trust in banks), and behaviorally specific (trust in the CDC is associated with protective behaviors but not visiting a doctor). The latter suggests that trust modifies voluntary compliance with public health recommendations rather than healthcare access or use. These findings identify institutional trust as a candidate stratifying variable for models of behavior-coupled transmission dynamics, and as a measurable target for public health surveillance.

Compartmental models of infectious disease transmission make assumptions about human behaviors. Specifically, they parameterize interactions across population groups, assumed to have distinct epidemiologically-relevant behavioral patterns, primarily through contact matrices stratified by demographic variables such as age, gender, or socioeconomic status. Although such demographic characteristics are readily measurable, they may inadequately capture the social and psychological forces that govern protective behaviors. Drawing on 20 waves of a national survey conducted throughout the COVID-19 pandemic in the United States, we show that institutional trust — particularly trust in public health agencies, physicians, and hospitals — is a dominant predictor of protective behavior adoption. For mask wearing during periods of strongest pandemic activity, for example, institutional trust explains more behavioral variance across population groups than age, income, education, and partisan affiliation combined. In unadjusted analyses, the difference in protective behavior adoption between individuals with the highest and lowest trust in the CDC was four- to six-fold larger than the corresponding differences by age, income, or educational attainment, and exceeded the difference between Democratic and Republican respondents. This association was institutionally specific (e.g., the relationship attenuates for trust in banks), and behaviorally specific (e.g., trust in the CDC is associated with protective behaviors but not visiting a doctor). The latter suggests that trust modifies voluntary compliance with public health recommendations rather than access to or use of healthcare. We conclude that compartmental models of disease transmission would be substantially improved by incorporating institutional trust as a stratifying variable. We additionally offer a trust-integrated mathematical modeling framework and recommendations for the data infrastructure needed for its implementation.

Public health crises demand timely surveillance of disease and interventions. This was a substantial challenge during COVID-19 in the United States due to the federalized response. States varied widely in infection and vaccination rates. Continuous state-level probability samples for tracking were unavailable and would have been logistically and financially onerous. Administrative data eventually became inaccurate. We thus evaluate the accuracy of large-scale opt-in non-probability state and national samples (from the Covid States Project) that estimated infection and vaccination rates on a near-continuous basis. The estimates aligned closely with a high-quality national probability sample and state-level administrative data (when such data were reliable). We offer evidence that the success of the surveys, compared to other less accurate non-probability surveys, may have stemmed from the successful recruitment of respondents with low trust in health institutions (i.e., individuals who often avoid surveys). We conclude by discussing how the Covid States Project can inform further data collection efforts requiring extensive spatial and temporal coverage.

Scientists provide important information to the public. Whether that information influences decision-making depends on trust. In the USA, gaps in trust in scientists have been stable for 50 years: women, Black people, rural residents, religious people, less educated people and people with lower economic status express less trust than their counterparts (who are more represented among scientists). Here we probe the factors that influence trust. We find that members of the less trusting groups exhibit greater trust in scientists who share their characteristics (for example, women trust women scientists more than men scientists). They view such scientists as having more benevolence and, in most cases, more integrity. In contrast, those from high-trusting groups appear mostly indifferent about scientists' characteristics. Our results highlight how increasing the presence of underrepresented groups among scientists can increase trust. This means expanding representation across several divides-not just gender and race/ethnicity but also rurality and economic status.

Background While the NIMH Research Domain Criteria framework stresses understanding how neuropsychiatric phenotypes vary across populations, little is known outside of small clinical cohorts about conspiratorial thoughts as an aspect of cognition. Methods We conducted a 50-state non-probability internet survey conducted in 6 waves between October 6, 2022 and January 29, 2024, with respondents age 18 and older. Respondents completed the American Conspiratorial Thinking Scale (ACTS) and the 9-item Patient Health Questionnaire (PHQ-9). Survey-weighted regression models were used to examine sociodemographic and clinical associations with ACTS score, and associations with vaccination status. Results Across the 6 survey waves, there were 123,781 unique individuals. After reweighting, a total of 78.6% of respondents somewhat or strongly agreed with at least one conspiratorial idea; 19.0% agreed with all four of them. More conspiratorial thoughts were reported among those age 25 - 54, males, individuals who finished high school but did not start or complete college, those with household income between $25,000 and $50,000 per year, and those who reside in rural areas, as well as those with greater levels of depressive symptoms. Endorsing more conspiratorial thoughts was associated with a significantly lower likelihood of being vaccinated against COVID-19. Discussion A substantial proportion of US adults endorsed at least some conspiratorial thinking, which varied widely across population subgroups. The extent of correlation with non-vaccination suggests the importance of considering such thinking in designing public health strategies.