The Daily Populous Sunday May 30th, 2021 evening edition
Authored by psypost.org and submitted by thebelsnickle1991

New research provides evidence that counties with higher levels of Trump support in 2016 fared worse than their non-Trump-supporting counterparts after implementing public health policies meant to prevent the spread of COVID-19. The study, which examined the early stages of the novel coronavirus pandemic, appears in the journal World Medical & Health Policy.

“This pandemic happened at a very special time in the United States with the presidential election going on,” said study author Jingjing Gao, a PhD candidate at The University of North Carolina at Charlotte. “The then president’s health policy preferences were different from health experts. We wanted to see whether political ideology played a role in the death outcomes at the beginning of this pandemic.”

The study utilized data from Johns Hopkins University’s COVID-19 Tracking Project and the 2016 U.S. Presidential Election. The researchers also used smartphone tracking data provided by SAFEGRAPH to estimate the effectiveness of stay-at-home policies from April 6 to May 25.

As expected, counties with a large population and a high percentage of elderly people tended to have greater death rates. County-level support for Trump by itself was not associated with COVID-19 death rates. However, the researcher found that “predicted rates of COVID-19-related deaths in counties with high levels Trump support increase along with the duration of implementation of several COVID-19 policies” such as stay-at-home orders.

In other words, after COVID-19 policies were put into place, the number of deaths per county increased more rapidly in counties with higher levels of Trump support than in counties with lower levels of Trump support.

“Sometimes people have to make personal choices about responding to a health crisis when they face mixed voices from politicians and health experts,” Gao told PsyPost. “Political polarization not only alienates Americans but it can also cause them to make decisions that kill them.”

The researchers found that individuals in counties with high levels of Trump support showed less compliance with stay-at-home policies, which suggests that “the positive interaction effects found between policy implementation duration and level of Trump support are likely the result of poor compliance with public health guidance,” the researchers said.

However, the link between Trump support and COVID-19 death rates did not appear to be related to noncompliance with stay-at-home policies in particular. Counties with a higher proportion the population staying completely at home tended to have greater COVID-19 death rates. “We suspect this may be due to reverse causality: compliance is higher in areas with greater coronavirus risk,” the researchers explained.

Instead, the link between Trump support and COVID-19 death rates might be a result of other types of noncompliance not captured by the study, such as “improper mask usage or failure to social distance in nonprofessional settings (e.g., parties or social gatherings),” the researchers added.

The findings are in line with another study published in the American Journal of Preventive Medicine, which found that per-capita rates of new COVID-19 cases and COVID-19 deaths were higher in states with Democrat governors in the first months of the pandemic in 2020, but became higher in states with Republican governors by mid-summer and through the rest of the year.

But the new study “only focuses on the first several months of this pandemic,” Gao noted. The findings may not generalize well beyond this timeframe. “We will have further research on the following period.”

The study, “Death by political party: The relationship between COVID-19 deaths and political party affiliation in the United States“, was authored by Jingjing Gao and Benjamin J. Radford.

dontsmoketheseeds on May 30th, 2021 at 15:40 UTC »

This doesn’t have to deal with science, just politics :(

Basilman121 on May 30th, 2021 at 15:22 UTC »

A few things need to be pointed out:

•This study only looked at the death rates during the start of the pandemic. Counties with higher Biden support later on would suffer significantly higher death rates later on, due to higher population density.

•Higher early on death rates among Trump supporters are likely due to age: COVID-19 is more dangerous for the older population compared with younger, more Democrat-leaning youth.

•Regardless, causation and correlation aren't suppose to be associated in such a manner.

Conclusion: this seems to me a political study masked in science. It isn't a great to push this sort of thing.

achesst on May 30th, 2021 at 14:53 UTC »

I'm hoping someone can help explain this study to me a bit better, as I'm confused by a few things in their methodology.

First, I was always under the impression that studies in general are trying to accept or reject a single null hypothesis. This study ends up listing ten different hypotheses that it will check from its dataset.

Hypothesis H1 (Political Affiliation): Counties with higher levels of Trump support will experience greater weekly COVID-19 death rates.

Hypothesis H2 (Policy Duration): The longer certain COVID-19 policies were in effect in a county, the fewer COVID-19 deaths the county will experience per week.

Hypothesis H2a The longer the implementation of a SIPO, the fewer deaths per week a county will experience.

Hypothesis H2b The longer the implementation of a public-school closure, the fewer deaths per week a county will experience.

Hypothesis H2c The longer the implementation of a dine-in restaurant closure, the fewer deaths per week a county will experience.

Hypothesis H2d The longer the implementation of an entertainment facility and gym closure, the fewer deaths per week a county will experience.

Hypothesis H2e The proportion of Trump supporters per county will mitigate the effect of policy duration on suppressing COVID-19 deaths.

Hypothesis H3a (Working modes): Counties with more people working from home tend to have fewer weekly COVID-19 deaths.

Hypothesis H3b (Working modes): Counties with more people working part-time from home tend to have fewer weekly COVID-19 deaths.

Hypothesis H3c (Working modes): Counties with more people working full time tend to have more weekly COVID-19 deaths.

Then, later in the study, we find this result: "While the coefficient for the level of Trump support is positive, it is not significant; we find no evidence for a relationship between supporter rate and county-level COVID-19 death rates (H1) after controlling for demographics, policy implementation, and working mode. However, the interaction effect between the level of Trump support per county and the duration of implementation of a SIPO is positive and statistically significant." However, this wasn't even one of their ten hypotheses they were initially testing for. I thought you were supposed to test your initial hypothesis against the data to see if it's significant, not manipulate the data into a form that finds significance.