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Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021

Nature Human Behaviour volume 7, pages 1708–1728 (2023)Cite this article

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Abstract

Epidemic disease can spread during mass gatherings. We assessed the impact of a type of mass gathering about which comprehensive data were available on the local-area trajectory of the COVID-19 epidemic. Here we examined five types of political event in 2020 and 2021: the US primary elections, the US Senate special election in Georgia, the gubernatorial elections in New Jersey and Virginia, Donald Trump’s political rallies and the Black Lives Matter protests. Our study period encompassed over 700 such mass gatherings during multiple phases of the pandemic. We used data from the 48 contiguous states, representing 3,108 counties, and we implemented a novel extension of a recently developed non-parametric, generalized difference-in-difference estimator with a (high-quality) matching procedure for panel data to estimate the average effect of the gatherings on local mortality and other outcomes. There were no statistically significant increases in cases, deaths or a measure of epidemic transmissibility (Rt) in a 40-day period following large-scale political activities. We estimated small and statistically non-significant effects, corresponding to an average difference of −0.0567 deaths (95% CI = −0.319, 0.162) and 8.275 cases (95% CI = −1.383, 20.7) on each day for counties that held mass gatherings for political expression compared to matched control counties. In sum, there is no statistical evidence of a material increase in local COVID-19 deaths, cases or transmissibility after mass gatherings for political expression during the first 2 years of the pandemic in the USA. This may relate to the specific manner in which such activities are typically conducted.

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Fig. 1: Overview of matching and estimation.
Fig. 2: Impact of all mass gatherings for political expression on COVID-19 mortality and case rates in the USA.
Fig. 3: Impact of the primary elections on COVID-19 mortality and case rates.
Fig. 4: Impact of the GA special election and the NJ and VA gubernatorial elections combined on COVID-19 mortality and case rates.
Fig. 5: Impact of Donald Trump’s rallies on COVID-19 mortality and case rates.
Fig. 6: Impact of the BLM protests on COVID-19 mortality.
Fig. 7: Impact of large-scale political events on COVID-19 Rt.
Fig. 8: Impact of large-scale political events on COVID-19 mobility.

Data availability

The data on COVID-19 case and death counts are available from the Johns Hopkins Coronavirus Resource Center (https://coronavirus.jhu.edu/data). US Census data are also publicly available (https://www.census.gov/programs-surveys/acs). The mobility tracking data are available from SafeGraph, Inc. and are freely available to academic researchers (https://www.safegraph.com/products/places). The elections turnout data are available directly from state governmental election agencies. The protest event data are available as the US Crisis Monitor dataset from the Armed Conflict Location & Event Data Project (https://acleddata.com/special-projects/us-crisis-monitor/). County-level masking data are available from The New York Times GitHub repository (https://raw.githubusercontent.com/nytimes/covid-19-data/master/mask-use/mask-use-by-county.csv).

Code availability

Data cleaning and preparation was carried out with the R programming language106. All analysis was conducted in the Julia programming language105 with the ‘TSCSMethods’ Julia package104 (https://github.com/human-nature-lab/TSCSMethods.jl). In addition, paper replication materials are available on GitHub as the ‘COVIDPoliticalEvents’ Julia package (https://github.com/human-nature-lab/COVIDPoliticalEvents.jl).

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Acknowledgements

We thank B. Snyder for help with data development and G. King for helpful comments. This research was supported by the Robert Wood Johnson Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

Author information

Authors and Affiliations

  1. Yale Institute for Network Science, Yale University, New Haven, CT, USA

    Eric Feltham, Laura Forastiere, Marcus Alexander & Nicholas A. Christakis

  2. Department of Sociology, Yale University, New Haven, CT, USA

    Eric Feltham & Nicholas A. Christakis

  3. Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA

    Laura Forastiere

  4. Frank H. Netter MD School of Medicine, Quinnipiac University, North Haven, CT, USA

    Marcus Alexander

  5. Department of Statistics and Data Science, Yale University, New Haven, CT, USA

    Nicholas A. Christakis

  6. Department of Medicine, Yale School of Medicine, New Haven, CT, USA

    Nicholas A. Christakis

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  1. Eric Feltham
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Contributions

E.F., L.F., M.A. and N.A.C. conceptualized the project. E.F., L.F., M.A. and N.A.C. developed the methodology. E.F. and L.F. performed statistical analysis. N.A.C. acquired funding. E.F., L.F., M.A. and N.A.C. wrote the paper.

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Correspondence to Eric Feltham.

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Extended data

Extended Data Fig. 1 Pre-outcome-window ATTs.

ATTs for the death and case rates, from 30 days before treatment up to the 9 days after treatment. The light green cell is the day of treatment, and the dark grey cell is the reference day for the ATT calculation (see Methods). In each panel, the error bars indicate the 95% CIs. The panels present the ‘average treatment effect on the treated’ (ATT) estimates for the (a) omnibus analysis, (b) the primary elections, (c) GA special election, (d) NJ & VA gubernatorial elections, (e) Donald Trump’s political rallies, and (f) the BLM protests. In each case, we observe results that are similar to those for the corresponding main analysis; that is, generally non-significant results, which is expected for the period prior to treatment.

Extended Data Fig. 2 Omnibus estimates for the effect of political events on the death rate.

(a) Overall ATT estimates and covariate balance before matching refinement. (The ATTs represent the average difference in the change in death rates, from the day before treatment to 10 to 40 days after an election. The pre-refinement covariate balance for each matching covariate. All covariates are measurements at the county level. The balance score is the average standardized mean difference between the treated and control units, over a matching period from 30 days before to 1 day before treatment. There are 2135 treated units present. (b) Overall ATT estimates and covariate balance after matching refinement, to no more than the five best matches to each treated county. (c) Overall ATT estimates and covariate balance before matching refinement and after the application of a caliper. (d) Overall ATT estimates and covariate balance after the application of a caliper, and after matching refinement, the observed balance scores are, on average over the matching window, within the threshold of 0.1, indicating sufficient similarity between the treated and matched counties. On average, for estimates over the outcome window, 1449.4 treated units remain, with 6084.5 matches.

Extended Data Fig. 3 Omnibus estimates for the effect of political events on the case rate.

(a) Overall ATT estimates and covariate balance before matching refinement. (The ATTs represent the average difference in the change in case rates, from the day before treatment to 10 to 40 days after an election. The pre-refinement covariate balance for each matching covariate. All covariates are measurements at the county level. The balance score is the average standardized mean difference between the treated and control units, over a matching period from 30 days before to 1 day before treatment. There are 2135 treated units present. (b) Overall ATT estimates and covariate balance after matching refinement, to no more than the five best matches to each treated county. (c) Overall ATT estimates and covariate balance before matching refinement and after the application of a caliper. (d) Overall ATT estimates and covariate balance after the application of a caliper, and after matching refinement, the observed balance scores are, on average over the matching window, within the threshold of 0.1, indicating sufficient similarity between the treated and matched counties. On average, for estimates over the outcome window, 1473.5 treated units remain, with 6305.4 matches.

Extended Data Fig. 4 Overall estimates for the primary elections.

In each panel, the error bars indicate the 95% CIs. (a) Overall ATT estimates and covariate balance before matching refinement. (The ATTs represent the average difference in the change in death rates, from the day before treatment to 10 to 40 days after an election. The pre-refinement covariate balance for each matching covariate. All covariates are measurements at the county level. The balance score is the average standardized mean difference between the treated and control units, over a matching period from 30 days before to 1 day before treatment. There are 1173 treated units present. (b) Overall ATT estimates and covariate balance after matching refinement, to no more than the five best matches to each treated county. (c) Overall ATT estimates and covariate balance before matching refinement and after the application of a caliper. (d) Overall ATT estimates and covariate balance after the application of a caliper, and after matching refinement, the observed balance scores are, on average over the matching window, within the threshold of 0.1, indicating sufficient similarity between the treated and matched counties. On average, for estimates over the outcome window, 961.2 treated units remain, with 4283.8 matched units.

Extended Data Fig. 5 Overall estimates for the GA elections.

In each panel, the error bars indicate the 95% CIs. (a) Overall ATT estimates and covariate balance before matching refinement. (The ATTs represent the average difference in the change in death rates, from the day before treatment to 10 to 40 days after an election. The pre-refinement covariate balance for each matching covariate. All covariates are measurements at the county level. The balance score is the average standardized mean difference between the treated and control units, over a matching period from 30 days before to 1 day before treatment. There are 159 treated units present. (b) Overall ATT estimates and covariate balance after matching refinement, to no more than the five best matches to each treated county. (c) Overall ATT estimates and covariate balance before matching refinement and after the application of a caliper. (d) Overall ATT estimates and covariate balance after the application of a caliper, and after matching refinement, the observed balance scores are, on average over the matching window, within the threshold of 0.1, indicating sufficient similarity between the treated and matched counties. On average, for estimates over the outcome window, 137 treated units remain, with 566 matched units.

Extended Data Fig. 6 Overall estimates for the NJ and VA gubernatorial elections.

In each panel, the error bars indicate the 95% CIs. (a) Overall ATT estimates and covariate balance before matching refinement. (The ATTs represent the average difference in the change in death rates, from the day before treatment to 10 to 40 days after an election. The pre-refinement covariate balance for each matching covariate. All covariates are measurements at the county level. The balance score is the average standardized mean difference between the treated and control units, over a matching period from 30 days before to 1 day before treatment. There are 154 treated units present. (b) Overall ATT estimates and covariate balance after matching refinement, to no more than the five best matches to each treated county. (c) Overall ATT estimates and covariate balance before matching refinement and after the application of a caliper. (d) Overall ATT estimates and covariate balance after the application of a caliper, and after matching refinement, the observed balance scores are, on average over the matching window, within the threshold of 0.1, indicating sufficient similarity between the treated and matched counties. On average, for estimates over the outcome window, 141 treated units remain, with 649 matched units.

Extended Data Fig. 7 Estimates for Donald Trump’s rallies, stratified by exposure (without caliper).

(a) Overall ATT estimates and covariate balance before matching refinement, for each stratum. (The ATTs represent the average difference in the change in death rates, from the day before treatment to 10 to 40 days after an election. The pre-refinement covariate balance for each matching covariate. All covariates are measurements at the county level. The balance score is the average standardized mean difference between the treated and control units, over a matching period from 30 days before to 1 day before treatment. The number of treated units in each stratum are on average, over the outcome window. For Treatment, there are 67 treated units; for Degree 1, there are 397 treated units; for Degree 2, there are 788 treated units; for Degree 3, there are 1156 treated units. (b) Overall ATT estimates and covariate balance after matching refinement, to no more than the five best matches to each treated county, for each stratum.

Extended Data Fig. 8 Estimates for Donald Trump’s rallies, stratified by exposure (with caliper).

In both panels, the error bars indicate the 95% CIs. (a) Overall ATT estimates and covariate balance before matching refinement, for each stratum. (The ATTs represent the average difference in the change in death rates, from the day before treatment to 10 to 40 days after an election. The pre-refinement covariate balance for each matching covariate. All covariates are measurements at the county level. The balance score is the average standardized mean difference between the treated and control units, over a matching period from 30 days before to 1 day before treatment. The number of treated units in each stratum are on average, over the outcome window. For Treatment, 58.2 treated units remain; for Degree 1, 363.4 treated units remain; for Degree 2, 713.3 treated units remain; for Degree 3, 1055.1 treated units remain. Respectively, with 251.8, 1679, 3290.9, 4280.4 matches. (b) Overall ATT estimates and covariate balance after matching refinement, to no more than the five best matches to each treated county, for each stratum.

Extended Data Fig. 9 Overall estimates for the BLM protests. In each panel, the error bars indicate the 95% CIs.

In each panel, the error bars indicate the 95% CIs. (a) Overall ATT estimates and covariate balance before matching refinement. (The ATTs represent the average difference in the change in death rates, from the day before treatment to 10 to 40 days after an event. The pre-refinement covariate balance for each matching covariate. All covariates are measurements at the county level. The balance score is the average standardized mean difference between the treated and control units, over a matching period from 30 days before to 1 day before treatment. On average, for estimates over the outcome window, 658 treated units are present. (b) Overall ATT estimates and covariate balance after matching refinement, to no more than the five best matches to each treated county. (c) Overall ATT estimates and covariate balance before matching refinement and after the application of a caliper. (d) Overall ATT estimates and covariate balance after the application of a caliper, and after matching refinement, the observed balance scores are, on average over the matching window, within the threshold of 0.1, indicating sufficient similarity between the treated and matched counties. On average, for estimates over the outcome window, 450.5 treated units remain, with 1901.7 matched units.

Extended Data Fig. 10 Political Event sizes.

(a) Overall distribution of event sizes in the data, across event type. Large frequencies for specific values reflect the thresholding procedure used to estimate crowd sizes from different reports (see Methods). (b) Overall distribution of event sizes in the data, across each event type, represented as the percentage of the county population in which the event takes place. (c) Event sizes over the roughly two-year period that constitutes our study horizon, colored by event type. Event sizes are plotted on the natural log scale, labelled on the original scale (persons at event).

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Feltham, E., Forastiere, L., Alexander, M. et al. Mass gatherings for political expression had no discernible association with the local course of the COVID-19 pandemic in the USA in 2020 and 2021. Nat Hum Behav 7, 1708–1728 (2023). https://doi.org/10.1038/s41562-023-01654-1

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