Partisan differences in physical distancing are linked to health outcomes during the COVID-19 pandemic

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We examined people’s physical distancing behaviours in 3,025 US counties between 9 March and 29 May 2020. Physical distancing was assessed via counties’ percentage reduction in general movement and percentage reduction in visiting non-essential services (for example, barbers, restaurants, clothing stores) relative to the period before COVID-19 struck the United States with force (before 9 March). The two physical distancing variables—general movement (mean (M) = 0.21, s.d. = 0.19; counties on average exhibited a 21% decrease in movement) and visiting non-essential services (M = 0.31, s.d. = 0.30; counties on average exhibited a 31% decrease in visiting non-essential services)—were calculated using approximately 15 million daily smartphone GPS coordinates across the United States (see Methods and Supplementary Information for details).

We first validated the two physical distancing measures by examining whether distancing increased as COVID-19 began to spread across the United States (March to early April 2020) but then decreased as states began to reopen (late April to May 2020). All models reported below are three-level, mixed-effects models where observations are nested within counties and counties within states. For the validation analyses, we report models with random intercepts of county and state and random slopes of linear and quadratic time (fitted using linear and quadratic orthogonal terms) at the state level (see Supplementary Information for more detail). The outcome variables were counties’ percentage reduction in general movement and percentage reduction in visiting non-essential services compared to pre-COVID-19 (before 9 March; Supplementary Note 1). Two-tailed statistical tests were used in all analyses.

As expected, general movement and visiting non-essential services reduced as COVID-19 first spread in the United States (early March into early April) and this reduction dissipated as states began to reopen (end of April through May). As seen in Fig. 1, changes in physical distancing over time were characterized by a negative quadratic effect (a convex distribution: unstandardized coefficient (B movement ) = −0.858, 95% confidence interval (CI) [–0.863, −0.854] and B visitation = −1.317, 95% CI [−1.325, −1.309], P < 0.001), and an additional negative linear trend (B movement = –0.336, 95% CI [−0.341, −0.331] and B visitation = –0.734, 95% CI [−0.741, −0.726], P < 0.001). Supplementary Information provides additional details of these and all following analyses.

Fig. 1: Physical distancing as a function of time (9 March to 29 May 2020). Percentage reduction in general movement and visiting non-essential services relative to before COVID-19 (before March 9). In line with the rapid implementation of physical distancing restrictions during mid-March 2020 and then weakening as US states began to reopen, physical distancing increased until early/mid-April and then began to decline. Each point represents one county (3,025 counties were included for general movement and 2,073 for non-essential services visitation across 82 days. The total number of data points was 248,050 and 169,890, respectively). The dashed line denotes the daily average across counties. The blue line represents the average prediction from a multi-level model including the effects of orthogonal linear and quadratic time terms, weekends, state stay-at-home policy, and county median income. Error bands are bootstrapped 95% prediction intervals (Supplementary Note 2). Full size image

We conducted additional analyses to further substantiate the validity of our physical distancing measures. As expected, distancing was greater when stay-at-home policies had been implemented (B movement = 0.055, 95% CI [0.053, 0.057] and B visitation = 0.062, 95% CI [0.059, 0.065], P < 0.001); on weekends, when fewer people travel for work (B movement = 0.088, 95% CI [0.087, 0.090] and B visitation = 0.026, 95% CI [0.025, 0.028], P < 0.001); and in higher-income counties, where people are able to work from home (B movement = 0.030, 95% CI [0.027, 0.032] and B visitation = 0.041, 95% CI [0.036, 0.046], P < 0.001; Supplementary Tables 19 and 20). The final model—including effects of orthogonal linear and quadratic time, weekends, state stay-at-home policy, and county median income—provided a good fit to the average trend in the data (Fig. 1). Model marginal R2 was 0.35 for the movement model and 0.37 for the visitation model (Supplementary Tables 19 and 20 and Supplementary Note 3). Together, these results suggest that the geotracking data utilized provide valid indices of physical distancing and allow us to test for differences as a function of political partisanship.

We examined whether county-level partisanship is linked to physical distancing. Partisanship was measured in terms of county-level voting gap in the 2016 election—the proportion of total votes for Donald Trump (Republican) minus the proportion of total votes for Hillary Clinton (Democrat). We tested three-level, mixed-effects models with random intercepts of state and county (county nested within state), random slope of partisanship at the county and state levels, random slopes of linear and quadratic time at the state level, and the random slope of the interaction between partisanship and linear time at the state level, when possible (Supplementary Note 4). Fixed effects included the orthogonal time terms (linear and quadratic), partisanship (vote gap), the interactions between partisanship and the two time terms, the weekend indicator, and key covariates likely to be associated with physical distancing, including counties’ COVID-19 infections per capita (cumulative; assessed daily), whether a stay-at-home policy was in effect on a specific day, party affiliation of state governor, population density, median income, income inequality, median age, age composition (proportion of population aged 0–17, >65 or >85 years; Supplementary Note 5), race/ethnicity (proportion of population Black, Hispanic and Asian), low access to grocery stores (for example, because of distance or income), number of grocery stores, travel time to work, religiosity, change in life expectancy from 1985 to 2010, and percentage employment. See Methods and Supplementary Information for details, descriptive statistics, and information on variable coding. Indicating that our findings are robust to different models, our results were also consistent when running models without these control variables (Supplementary Tables 22 and 23).

We found that the more a county favoured Donald Trump over Hillary Clinton in the 2016 election, the less that county exhibited physical distancing between 9 March and 29 May 2020. Specifically, for every 1 percentage point increase in vote share for Donald Trump over Hillary Clinton, counties exhibited 0.11 percentage points less physical distancing in terms of reducing their general movement and 0.13 percentage points less physical distancing in terms of reducing their visiting of non-essential services (B movement = –0.114, 95% CI [–0.140, –0.087] and B visitation = –0.125, 95% CI [–0.162, –0.088], P < 0.001; Table 1; visualization of physical distancing by specific location and partisanship is presented in Figs. 2 and 3). Model marginal R2 was 0.46 for the movement model and 0.54 for the visitation model (Supplementary Note 6).

Table 1 Results of the main multi-level mixed-effects models in terms of reduction in general movement and visiting non-essential services Full size table

Fig. 2: Map of predicted percent reduction in general movement as a function of county location and leaning pro-Trump versus pro-Clinton. Predictions are derived from the multi-level models described in Table 1 and the main text. Each circle represents a county, its size proportional to its population. Lighter and darker colours indicate less or greater physical distancing, respectively. Blue indicates a Clinton-leaning county and red indicates a Trump-leaning county, based on 2016 voting figures. Full size image

Fig. 3: Map of predicted reduction in visiting non-essential services as a function of county location and leaning pro-Trump versus pro-Clinton. Predictions are derived from multi-level models described in Table 1 and the main text. Each circle represents a county, its size proportional to its population. Lighter and darker colours indicate less or greater physical distancing, respectively. Blue indicates a Clinton-leaning county and red indicates a Trump-leaning county, based on 2016 voting figures. Full size image

Collapsing counties into pro-Trump versus pro-Clinton bins, Trump-voting counties reduced their general movement 9.5 percentage points less and reduced their visiting of non-essential services 19.4 percentage points less than Clinton-voting counties (average reduction, 14.5 percentage points) across the study duration. Illustrating the relative power of the observed links, partisanship was more strongly associated with physical distancing in our main models (when z-scoring all the included variables) than any of the other included variables (aside from the time terms, the weekend factor, and median age in the case of visitation; see z-scored models in our analysis outputs hosted at https://osf.io/u5pmw/?view_only=33f0691a7e694276bef606cb3e22d141; Supplementary Note 7). To put this into context, partisanship was more strongly associated with distancing than counties’ number of COVID-19 cases per capita, median income, percentage employment, average travel time to work, governor political affiliation, and racial make-up, as well as the other variables noted above.

Additionally demonstrating the robustness of our findings, partisanship was associated with reduced physical distancing even after adjusting for the interactions between each of the included covariates and partisanship (Supplementary Tables 22 and 23), when including in the analyses counties’ percentage of employment in various types of profession (for example, agriculture, finance, manufacturing; Supplementary Table 24), when adding specific state policies to the analyses (for example, closing restaurants, closing childcare centres; Supplementary Tables 32 and 33), and when running generalized additive models with time as a smoothed variable (Supplementary Table 25 and Supplementary Note 8).

We next examined how partisan differences in physical distancing changed over time (between March and May 2020). We predicted that the observed differences would decrease as the pandemic worsened and people became more aware of the dangers of COVID-19 (see preregistration of this hypothesis at http://aspredicted.org/blind.php?x=n84tw2; Supplementary Notes 9 and 10). We found the opposite pattern. As indicated by negative interaction terms between vote gap and linear time in the models noted above (B movement = −0.382, 95% CI [−0.396, −0.367] and B visitation = –1.030, 95% CI [−1.054, −1.005], P < 0.001; Table 1), the partisan gap in physical distancing actually increased rather than decreased between 9 March and 29 May 2020, and this was especially true for visiting non-essential services according to model predictions (Fig. 4 and Supplementary Note 11).

Fig. 4: US counties’ average physical distancing as a function of time and partisanship. Physical distancing was quantified as counties’ percent reduction in general movement and visiting non-essential services as compared to before COVID 19 (before March 9). For graphing purposes, counties were binned as Trump lean if a greater percentage of residents voted for Trump in 2016, and as Clinton lean if a greater percentage of residents voted for Clinton. Each point represents one county (3,007 were included for general movement and 2,068 for non-essential services visitation across 82 days; the number of total data points was 246,574 and 169,480, respectively). Dashed lines denote the daily average across counties, split by partisan lean. Bold blue and red lines represent the average predictions of vote gap from the multi-level models described in Table 1 and the main text. Error bands are bootstrapped 95% prediction intervals. Full size image

We also examined the link between partisanship and physical distancing over time in a different manner. We conducted a series of mixed-effects models to extract the specific coefficients for the link between partisanship and physical distancing (general movement and non-essential visitations, multiplied by 100 to yield percentages) on each day from 9 March to 29 May. All of the covariates noted above were included in these models. We then plotted the extracted coefficients over time (Fig. 5). Linear models predicting these coefficients with orthogonal linear and quadratic time terms (z-scored) indicated that the negative association between pro-Trump voting and physical distancing (collapsed across the two measures) strengthened by approximately −0.047 percentage points per day (–3.85 percentage points beteen 9 March and 29 May; linear time term: B movement = −0.96, 95% CI [−1.15, −0.77] and B visitation = −1.30, 95% CI [−1.60, −1.00], P < 0.001). We also observed a quadratic pattern for general movement. Pro-Trump voting increasingly linked to decreased physical distancing as the pandemic progressed, finally peaking in early May and beginning to weaken slightly thereafter (quadratic time effect: B movement = 0.86, 95% CI [0.67, 1.05], P < 0.001; B visitation = 0.36, 95% CI [0.06, 0.67], P = 0.019; Fig. 5 and Supplementary Note 12). These analyses further confirm that the observed partisan gap in distancing increased over time.

Fig. 5 Percentage reduction in general movement and visiting non-essential services (relative to pre-COVID, before 9 March) for Trump as compared to Clinton voting counties (negative values indicate less physical distancing in Trump counties) as a function of time (between 9 March and 29 May 2020). Individual points represent regression coefficients. More negative coefficients represent a greater partisan gap. For instance, –6 on the yaxis indicates 6 percentage points less physical distancing in Trump counties (+1s.d. pro-Trump vote lean) on a specific day. Given the date range of 82days, 82observations are included for each of the two physical distancing measures. Blue lines represent predictions from linear regression models with linear and quadratic time terms. Generalized additive models models of these data can be found in the Supplementary Information. Error bands, mean±2s.e. Full size image

Partisanship, physical distancing, and state policies

Possibly, local stay-at-home policies attenuate the observed partisan differences in physical distancing because these policies send out clear signals about the dangers of COVID-19. In line with this reasoning, we predicted that state-level stay-at-home orders would potentially reduce the observed partisan differences (see our preregistered hypotheses). Our data did not support this prediction. The link between voting for Trump and reduced physical distancing was actually stronger when stay-at-home orders were in effect, as evidenced by negative interactions between vote gap (with higher values indicating greater pro-Trump lean) and state policy: B movement = −0.017, 95% CI [−0.022, −0.013], P < 0.001 and B visitation = −0.009, 95% CI [−0.016, −0.001], P = 0.030 (examinations of detailed state policies, such as closing restaurants and closing schools, are included in Supplementary Tables 32 and 33 and Supplementary Note 13). These results align with recent work finding that Democratic counties responded more quickly to stay-at-home orders than Republican counties at the start of the pandemic32.

We considered the possibility that variability in state stay-at-home policies explains our findings rather than county-level partisanship. For instance, stay-at-home orders may have been more stringent and remained in place longer in Democratic states or in states with Democratic governors, in turn leading Republican-leaning counties (which are more common in Republican states) to exhibit less physical distancing. Three supplementary analyses argued against this possibility, however. First, our results remained when adjusting for whether state policies were in effect on a specific date, state governors’ political affiliation, and interactions between these variables and partisanship (Table 1 and Supplementary Table 31). Second, we also confirmed that our results remained when adding specific state policies (including closing restaurants, closing childcare, closing K–12 schools, closing non-essential business more generally, and closing religious institutions/gatherings), and the interactions between these policies and governor political party and partisanship, to our models (see Detailed State Policies section in the Supplementary Information and Supplementary Tables 32 and 33). And third, while there was some variation in our findings between states, most states were in line with the population average of the observed partisan differences (Supplementary Figs. 4–6 and Supplementary Note 14). In sum, though differences in state policies contribute to variation in county-level physical distancing, our results indicate that county-level partisanship nonetheless explains unique variance in physical distancing (see Supplementary Information for more details).

One method through which partisan viewpoints are propagated is partisan media. Potentially, then, watching more Republican- than Democratic-leaning media (for example, Fox News versus MSNBC) is linked to lower levels of physical distancing. Indeed, recent research has found a causal effect of Fox News viewership on reducing stay-at-home behaviour33 and indicated that behavioural responses to COVID-19 change in line with the specific cable news show that people watch (for example, Tucker Carlson Tonight versus Hannity)34. To examine whether viewing Republican- versus Democratic-leaning media is associated with differences in physical distancing, we analysed viewership data from SimplyAnalytics. We found that the degree to which people in different counties watched Fox News over CNN and MSNBC was associated with lower levels of physical distancing (B movement = −0.053, 95% CI [−0.076, −0.030] and B visitation = –0.099, 95% CI [−0.136, −0.062], P < 0.001; Supplementary Fig. 2 and Supplementary Note 15). Importantly, these results were found while accounting for all covariates (Main Model in Supplementary Tables 27 and 28) and remained when additionally controlling for interactions between watching Fox News (over CNN and MSNBC) and these covariates: P < 0.001 (Saturated Model in Supplementary Tables 27 and 28). And, as was the case for partisanship as assessed by 2016 vote gap, the observed partisan media differences in physical distancing strengthened over time (Supplementary Tables 27, 28, and 30 and Supplementary Figs. 2 and 3).

We also tested whether consuming Republican-leaning media and 2016 pro-Trump versus pro-Clinton voting account for independent variance in physical distancing. Supporting this possibility, we found that Fox News consumption (over MSNBC and CNN) still negatively related to physical distancing when including 2016 vote gap, the interactions between vote gap and linear and quadratic time, and the interaction between vote gap and Fox News lean in the models: B visitation = –0.038, 95% CI [−0.063, −0.014], P = 0.002 and B visitation = −0.053, 95% CI [−0.092, −0.013], P = 0.009 (Supplementary Table 29). Furthermore, in these analyses the coefficients for partisan vote gap and Fox News lean both decreased (Table 1 and Supplementary Tables 27 and 29), suggesting that Republican-leaning media consumption may in part account for the observed covariance between vote gap and physical distancing (and vice versa).

Linking physical distancing to COVID-19 infections and fatalities

We next examined whether the observed partisan differences in distancing relate to COVID-19 infections and fatalities. Applying three-level, mixed-effects models, with counties nested within states, we first tested whether counties’ time-lagged physical distancing is associated with decreased COVID-19 infection and fatality growth rate. Growth rates were examined because the main goal of physical distancing is to ‘flatten the curve’, or the spread, of coronavirus disease (see Methods for calculation)34,35.

Given the time lag between contracting COVID-19, experiencing symptoms, and receiving a positive test result, physical distancing should not have an immediate impact on infection or fatality growth rate. In line with this reasoning, we considered the length of the delay between physical distancing and subsequent reductions in infections and fatalities. We treated the incubation period of COVID-19 as the time from exposure to symptom onset36,37,38,39. Although this period may include a period of asymptomatic viral shedding, we did not account for this in our analysis39. Based on previous work, we estimated the incubation period of COVID-19 to be between 2 and 11 days (median 4–5 days)36,37,38,39. We then estimated that receiving positive test results typically takes at least 1 day (and often takes much longer). Given these estimates, physical distancing could begin to reduce counties’ infection growth rate as early as 3 days thereafter, but more likely begins to have a measurable impact around 7–8 days later (~5-day incubation period plus ~3 days to seek and receive test results). Importantly, this estimate does not represent when the impact of physical distancing is strongest. That is, physical distancing should relate to decreased infection growth rate more and more strongly as delayed testing is completed, residual infections die out, and cases with long incubation periods pass, until eventually this link peaks and then starts to decline (Supplementary Figs. 12–15)40.

We conducted a series of mixed-effects models to identify the lag time in which physical distancing most strongly relates to reduced infections. Specifically, we examined the predictive power of rolling 7-day averages of general movement and visiting non-essential services ((p t + p t – 1 + … + p t – 6 )/7; t = {35, 34, …, 7}), where t is lag day and p is physical distancing score) on infection growth rate. Both for general movement and visiting non-essential services, physical distancing between 17 and 23 days before a given date (z-scored, 7-day lagged average) most strongly related to lower infection growth rate on that date: B movement = −3.05, 95% CI [−3.15, −2.94] and B visitation = −3.53, 95% CI [−3.65, −3.41], P < 0.001 (Table 2 and Supplementary Figs. 12 and 13; the first 7-day lag to negatively relate to infections was between 6 and 12 days for general movement and 7 and 13 days for visitation). In other words, a reduction in infection growth rate on a specific day was best accounted for by the degree to which people in a county were physically distancing between 17 and 23 days earlier (Supplementary Information).

Table 2 Results of multi-level, mixed-effects models testing whether physical distancing is linked to infection and fatality growth rates Full size table

Importantly, this link remained when including all covariates from our earlier analyses (for example, population density, distance travelling to work, age; see Methods) and when additionally including non-time-lagged physical distancing (physical distancing on the same day) in these models: B movement = −3.31, 95% CI [−3.42, −3.21] and B visitation = −4.54, 95% CI [−4.66, −4.41], P < 0.001 (Table 2). Moreover, supporting the unique relationship between time-lagged physical distancing and subsequent infections, counties’ non-lagged physical distancing (that is, same-day physical distancing) was associated with a higher rather than lower infection growth rate: B movement = 1.66, 95% CI [1.55, 1.77] and B visitation = 2.94, 95% CI [2.81, 3.08], P < 0.001 (Table 2). These positive same-day relationships between distancing and growth rate likely reflect that counties hit hardest by the pandemic were also those that responded by engaging most in physical distancing (see Supplementary Information for cross-lagged analyses substantiating this claim).

Turning to fatalities, estimates of the time lag from COVID-19 infection to death vary between 2 and 4 weeks (approximately 1–2 weeks after symptoms appear)41,42. Using the same 7-day lagged approach used for infections, we found that counties’ physical distancing 25–31 days before a given day most strongly related to lower fatality growth on that day: B movement = –0.67, 95% CI [–0.74, –0.59] and B visitation = −0.69, 95% CI [−0.79, −0.59], P < 0.001 (Table 2 and Supplementary Figs. 14 and 15). As was true for infections, these results remained when including all covariates and when including non-lagged physical distancing in the models: B movement = −0.70, 95% CI [–0.78, −0.62] and B visitation = –0.81, 95% CI [−0.91, −0.70], P < 0.001 (Table 2). And, as was also true for infections, non-lagged distancing was associated with higher rather than lower fatalities: B movement = 0.45, 95% CI [0.38, 0.51] and B visitation = 0.55, 95% CI [0.46, 0.64], P < 0.001 (Table 2). Collectively, these results suggest that counties’ degree of physical distancing 17–23 and 25–31 days earlier best accounts for reductions in infection and fatality growth rates, respectively.

Linking partisanship and physical distancing to COVID-19 infections and fatalities

Finally, we tested whether partisan-linked failures to physically distance during the pandemic related to higher infection and fatality growth rates. We approached this analysis by testing mediation models in Stata following multi-level mediation procedures43. These mediation models included three-level, mixed-effects models as in the analyses reported throughout this article (Supplementary Note 16). The models included counties’ pro-Trump voting (z-scored) as the predictor variable, lagged physical distancing (general movement and visiting non-essential services lagged 17–23 days for infections and 25–31 days for fatalities; z-scored) as the mediators, infection growth rate or fatality growth rate as the outcome variables and all covariates as control variables (see Supplementary Information for more detail).

The mediation analyses revealed significant indirect links between counties’ pro-Trump voting and infection growth rate via physical distancing (lagged 17–23 days): B movement = 0.855, 95% CI [0.622, 1.088] and B visitation = 0.896, 95% CI [0.679, 1.113], P < 0.001 (Table 3). Regarding total effects, Trump-leaning counties on average exhibited marginally higher infection growth rates than Clinton-leaning ones between 26 March and 29 May 2020: B movement = 0.272, 95% CI [−0.020, 0.564] and B visitation = 0.313, 95% CI [0.032, 0.594], P < 0.068 (Total Effects in Table 3 and Supplementary Note 17); however, this would not have been the case if Trump-leaning counties had physically distanced to the same degree as more Clinton-leaning counties. If they had done so, Trump-leaning counties would actually have had lower infection growth rates than Clinton-leaning ones between 26 March and 29 May 2020: B movement = −0.583, 95% CI [−0.915, −0.251] and B visitation = −0.582, 95% CI [–0.912, –0.253], P < 0.002 (Direct Effects in Table 3).

Table 3 Output of mediation models Full size table

Similar indirect effects were also observed for fatality growth rate: B movement = 0.183, 95% CI [0.090, 0.277] and B visitation = 0.176, 95% CI [0.087, 0.264], P < 0.001 (lagged 25–31 days earlier; Table 3). Regarding total effects, Trump-leaning counties exhibited lower fatality growth rates than Clinton-leaning ones: B movement = −0.417, 95% CI [−0.647, −0.188] and B visitation = −0.533, 95% CI [−0.850, −0.216], P < 0.001 (between 3 April and 29 May 2020; see Total Effects in Table 3); however, these counties would have exhibited even lower fatality growth rates had they physically distanced to the same extent as more Clinton-leaning counties: B movement = −0.601, 95% CI [−0.858, −0.344] and B visitation = −0.709, 95% CI [−1.050, −0.368], P < 0.001 (see Direct Effects in Table 3 and Supplementary Note 18).

This pattern of results was robust. The observed mediations remained consistent when including employment types as further control variables (P < 0.003; Supplementary Table 34); when testing earlier date ranges for the lagged mediators (for example, 26 March to 29 May for infections), P < 0.005 (Supplementary Table 35); when excluding all cases in which growth rate was 0 (to account for potential zero inflation in the data), P < 0.001 (Supplementary Table 36); when calculating infection and fatality growth rates using moving averages, P < 0.001 (Supplementary Table 37); and when including specific state policies (for example, closing restaurants, closing schools) as additional control variables, P < 0.001 (Supplementary Table 38).

To put our findings into context, our model indicated that extremely pro-Trump-voting counties (+2 z-score in the vote gap variable) experienced a daily infection growth rate of 0.59 percentage points higher than average (between 26 March and 29 May 2020, the average daily infection growth rate across all counties in this date range was 5.72 percentage points; Supplementary Note 19). Such pro-Trump counties would have experienced a daily infection growth rate of 1.17 percentage points less on average, however, if they had socially distanced to the same extent as the average county—a swing of 1.76 percentage points in infection growth rate. Our findings thus suggest that partisan differences in physical distancing were linked to higher growth rates of infections and fatalities in pro-Trump counties than necessary.

Finally, we reconducted our mediation analyses when calculating infection and fatality growth rate using an exponential model of disease spread (Supplementary Information; growth rate r = ln(I t + 1 /I t ), where I is the cumulative number of infections and t is the time (day) (ref. 35)). Applying an exponential growth rate is illuminating from an epidemiological perspective, because COVID-19 would initially spread exponentially in the scenario of a disease spreading within a closed, homogeneous population without interventions29,35,44,45. Importantly, when doing so, we observed the same mediation results as above: P < 0.001 (Supplementary Table 39).

It is possible that the estimated indirect effects are skewed by differences in reporting or tracking infections (for example, certain areas testing more than others). To begin to account for this possibility, we examined and found that the observed indirect effects of county partisanship on infection growth rate remained when individually testing these mediations within states and then aggregating these results via meta-analysis: B = 0.417, z = 3.43, P < 0.001 (Supplementary Information). These analyses help address reporting concerns because, although reporting differences may exist across states, it is less likely that they exist within states (the utilized mixed-effects modelling should also help allay this concern; Supplementary Note 20). Nonetheless, biased estimates in reporting and testing remain possible, if not probable, and thus are a limitation of our results.

SharkOnGames on November 2nd, 2020 at 23:15 UTC »

Out of curiosity, is there any truth to the statement that there is a higher number of conservatives in the older age groups vs the younger age groups?

We know that about 50% of all covid-19 related deaths happened in nursing homes or elderly care homes.

This could skew results significantly in a study like the above.

GreenPlasticJim on November 2nd, 2020 at 22:56 UTC »

This trend seems to be captured in similar magnitude in the Florida early voting totals. As of today 57% of the votes cast by registered Republicans have been in person early voting (rather than mail-in) compared to about 40% for registered Democrats.

Edit: https://countyballotfiles.floridados.gov/VoteByMailEarlyVotingReports/PublicStats

NewReddit101 on November 2nd, 2020 at 22:22 UTC »

I found the gps data that (I think) they used on Unacast’s website, which is publicly available and is pretty interesting https://www.unacast.com/covid19/social-distancing-scoreboard