# Socioeconomic Correlates of Anti-Science Attitudes in the US
## 3 Results
### Anti-Science Attitudes in the Age of COVID-19  
Response to the COVID-19 pandemic in the US showed how political partisanship [5] and the mistrust of science [7] contributed to the resistance to COVID-19 mitigation measures, such as social distancing, lockdowns, and wearing masks [6]. More recently, the movement against COVID restrictions has found a new cause in resistance to the COVID-19 vaccines. At the time of writing, although many Americans have received a COVID-19 vaccine, a substantial number remain reluctant to receive their shot, endangering the goal of herd immunity. Analysts looking for sources of vaccine hesitancy identified a mistrust of government and the role of political partisanship [43]. Additionally, the mistrust of science and conspiratorial thinking also fuels the anti-vaccine movement [7]. Here, we examine the relationship between anti-science attitudes (in 2016) and vaccination rates across the country (as of 6 June 2021). Despite the long gap in time, in some communities, we found that anti-science attitudes were significantly associated with lower vaccination rates. Multi-variate regression of the pooled county data (Figure 5 (left)) shows that the share of anti-science users within a county is negatively associated with that county's vaccination rates, even after controlling for partisanship ([PERSON] voter share in 2016) and other socioeconomic factors. All variables, except for education, are found to be statistically  
Figure 4: Descriptive statistics of the components (groups) discovered in the anti-science data. (**left**) Average value of each explanatory variable across counties assigned to that group. (**right**) Distribution of the urban–rural continuum codes for the counties assigned to each discovered group. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DogR Result** & **\# of Counties** & \(\mathbf{R^{2}}\) & **Adjusted \(\mathbf{R^{2}}\)** \\ \hline \multirow{3}{*}{Anti-science share} & Pooled & 784 & 0.424 & 0.418 \\  & Group 0 & 330 & 0.488 & 0.473 \\  & Group 1 & 340 & 0.591 & 0.580 \\  & Group 2 & 114 & 0.357 & 0.302 \\ \hline \multirow{3}{*}{Vaccination share} & Pooled & 716 & 0.444 & 0.436 \\  & Group 0 & 158 & 0.533 & 0.501 \\ \cline{1-1}  & Group 1 & 386 & 0.686 & 0.678 \\ \cline{1-1}  & Group 2 & 172 & 0.311 & 0.268 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Goodness-of-fit measures for regression in pooled data containing all counties and for the subgroups of counties discovered by DoGR. significant: as older, whiter, more affluent counties have higher vaccination rates, those with larger households have lower vaccination rates. Upon further examination, we used DoGR to identify the latent groups that best explain COVID-19 vaccination rates as a dependent variable. The data are best explained by three groups (according to BIC): (Group 0) the bigger, more affluent, and better-educated counties, (Group 1) the whiter, older, more pro-Trump communities, and (Group 2) counties with larger household and bigger share of anti-science users (Figure 6 (left)). This latter group had the lowest average vaccination rate, with only 30% of residents fully vaccinated, compared to 65% and 59% for the first two groups. As shown by the distribution of the county's rural-urban continuum codes (Figure 6 (right)), the first group represents the largest metro areas, and while the last two components are fairly similar, though Group 1 is more rural. Although the latter two groups are similar according to the rural-urban continuum measure, their population has different behaviors, suggesting additional cultural differences not accounted for in our analysis, for example, the north-south divide. Regression coefficients within these groups (Figure 5 (right)) tell a more nuanced story. The anti-science share is associated with lower vaccination rates in urban communities (statistically significant relationship for Group 0) and mostly rural communities (Group 2, but not statistically significant).