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Feltham E, Forastiere L, Alexander M, Christakis NA. 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 2023; 7:1708-1728. [PMID: 37524931 DOI: 10.1038/s41562-023-01654-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 06/14/2023] [Indexed: 08/02/2023]
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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Affiliation(s)
- Eric Feltham
- Yale Institute for Network Science, Yale University, New Haven, CT, USA.
- Department of Sociology, Yale University, New Haven, CT, USA.
| | - Laura Forastiere
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Marcus Alexander
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Frank H. Netter MD School of Medicine, Quinnipiac University, North Haven, CT, USA
| | - Nicholas A Christakis
- Yale Institute for Network Science, Yale University, New Haven, CT, USA
- Department of Sociology, Yale University, New Haven, CT, USA
- Department of Statistics and Data Science, Yale University, New Haven, CT, USA
- Department of Medicine, Yale School of Medicine, New Haven, CT, USA
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Homan P, Levy I, Feltham E, Gordon C, Hu J, Li J, Pietrzak RH, Southwick S, Krystal JH, Harpaz-Rotem I, Schiller D. Author Correction: Neural computations of threat in the aftermath of combat trauma. Nat Neurosci 2019; 22:840-841. [DOI: 10.1038/s41593-019-0356-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Homan P, Levy I, Feltham E, Gordon C, Hu J, Li J, Pietrzak RH, Southwick S, Krystal JH, Harpaz-Rotem I, Schiller D. Neural computations of threat in the aftermath of combat trauma. Nat Neurosci 2019; 22:470-476. [PMID: 30664770 DOI: 10.1038/s41593-018-0315-x] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 12/04/2018] [Indexed: 01/28/2023]
Abstract
By combining computational, morphological, and functional analyses, this study relates latent markers of associative threat learning to overt post-traumatic stress disorder (PTSD) symptoms in combat veterans. Using reversal learning, we found that symptomatic veterans showed greater physiological adjustment to cues that did not predict what they had expected, indicating greater sensitivity to prediction errors for negative outcomes. This exaggerated weighting of prediction errors shapes the dynamic learning rate (associability) and value of threat predictive cues. The degree to which the striatum tracked the associability partially mediated the positive correlation between prediction-error weights and PTSD symptoms, suggesting that both increased prediction-error weights and decreased striatal tracking of associability independently contribute to PTSD symptoms. Furthermore, decreased neural tracking of value in the amygdala, in addition to smaller amygdala volume, independently corresponded to higher PTSD symptom severity. These results provide evidence for distinct neurocomputational contributions to PTSD symptoms.
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Affiliation(s)
- Philipp Homan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ifat Levy
- Departments of Comparative Medicine, Neuroscience and Psychology, Yale University, New Haven, CT, USA
| | - Eric Feltham
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,US Department of Veterans Affairs National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Charles Gordon
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,US Department of Veterans Affairs National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Jingchu Hu
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jian Li
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing, China
| | - Robert H Pietrzak
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,US Department of Veterans Affairs National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Steven Southwick
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,US Department of Veterans Affairs National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - John H Krystal
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.,US Department of Veterans Affairs National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA
| | - Ilan Harpaz-Rotem
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA. .,US Department of Veterans Affairs National Center for PTSD, Clinical Neurosciences Division, VA Connecticut Healthcare System, West Haven, CT, USA.
| | - Daniela Schiller
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. .,Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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