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Nishimi K, Borsari B, Marx BP, Rosen RC, Cohen BE, Woodward E, Maven D, Tripp P, Jiha A, Woolley JD, Neylan TC, O'Donovan A. Clusters of COVID-19 protective and risky behaviors and their associations with pandemic, socio-demographic, and mental health factors in the United States. Prev Med Rep 2021; 25:101671. [PMID: 34926133 PMCID: PMC8669937 DOI: 10.1016/j.pmedr.2021.101671] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Revised: 11/02/2021] [Accepted: 12/12/2021] [Indexed: 11/11/2022] Open
Abstract
Protective and risky behaviors for COVID-19 cluster in a U.S.-based sample. Behavior classes had differing patterns of socio-demographics and pandemic exposure. Posttraumatic stress and anxiety were elevated among protective and risky classes.
Individual behaviors are critical for preventing the spread of coronavirus disease 2019 (COVID-19) infection. Given that both protective and risky behaviors influence risk of infection, it is critical that we understand how such behaviors cluster together and in whom. Using a data-driven approach, we identified clusters of COVID-19-related protective and risky behaviors and examined associations with socio-demographic, pandemic, and mental health factors. Data came from a cross-sectional online U.S. nationwide study of 832 adults with high levels of pre-pandemic trauma. Latent class analysis was performed with ten protective (e.g., washing hands, wearing masks) and eight risky (e.g., attending indoor restaurants, taking a flight) behaviors for COVID-19. Then, we examined distributions of socio-demographic and pandemic factors across behavior classes using ANOVA or Chi-square tests, and associations between mental health factors (depressive, anxiety, posttraumatic stress symptoms) and behavior classes using multinomial logistic regression. We identified four classes, including three classes with relatively low risky but high (28.8%), moderate (33.5%) and minimal (25.5%) protective behaviors and one high risky behaviors class with associated moderate protective behaviors (12.1%). Age, sexual orientation, political preference, and most pandemic factors differed significantly across behavior classes. Anxiety and posttraumatic stress symptoms, but not depression, were higher in the High Risk, but also Highly and Moderately Protective classes, relative to Minimally Protective. Prevention and intervention efforts should examine constellations of protective and risky behaviors to comprehensively understand risk, and consider current anxiety and posttraumatic stress symptoms as potential risk indicators.
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Key Words
- ANOVA, analysis of variance
- AWE, Approximate Weight of Evidence Criterion
- AvePP, average posterior class probability
- BF, Bayes Factors
- BIC, Bayesian Information Criterion
- COVID-19
- COVID-19, coronavirus disease 2019
- DASS, Depression Anxiety Stress Scale
- LCA, latent class analysis
- Latent class analysis
- Mental health
- OCC, odds of correct classification
- PTSD Checklist-5, PCL-5
- PTSD, posttraumatic stress disorder
- Protective behaviors
- Risky behaviors
- cAIC, consistent Akaike's Information Criterion
- mcaP, modal class assignment proportion
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Affiliation(s)
- Kristen Nishimi
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA.,Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Brian Borsari
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA.,Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Brian P Marx
- National Center for PTSD, VA Boston Healthcare System, Boston, MA, USA.,Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA
| | - Raymond C Rosen
- Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Beth E Cohen
- Department of Medicine, University of California San Francisco, San Francisco, CA, USA.,Medical Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA
| | - Eleanor Woodward
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA.,Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - David Maven
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA.,Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Paige Tripp
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA.,Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Ahmad Jiha
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA.,Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Joshua D Woolley
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA.,Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Thomas C Neylan
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA.,Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Aoife O'Donovan
- Mental Health Service, San Francisco Veterans Affairs Healthcare System, San Francisco, CA, USA.,Department of Psychiatry and Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
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Norscia I, Zanoli A, Gamba M, Palagi E. Auditory Contagious Yawning Is Highest Between Friends and Family Members: Support to the Emotional Bias Hypothesis. Front Psychol 2020; 11:442. [PMID: 32317997 PMCID: PMC7147458 DOI: 10.3389/fpsyg.2020.00442] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Accepted: 02/25/2020] [Indexed: 12/30/2022] Open
Abstract
Contagious yawning differs from spontaneous yawning because it occurs when an individual yawns in response to someone else’s yawn. In Homo sapiens and some non-human primates contagious yawning is higher between strongly than weakly bonded individuals. Up to date, it is still unclear whether this social asymmetry underlies emotional contagion (a basic form of empathy preferentially involving familiar individuals) as predicted by the Emotional Bias Hypothesis (EBH) or is linked to a top-down, selective visual attention bias (with selective attention being preferentially directed toward familiar faces) as predicted by the Attentional Bias Hypothesis (ABH). To verify whether the visual attentional bias explained the yawn contagion bias or not, in this study, we considered only yawns that could be heard but not seen by potential responders (auditory yawns). Around 294 of auditory yawning occurrences were extrapolated from over 2000 yawning bouts collected in free ranging humans for over nine years. Via GLMM, we tested the effect of intrinsic features (i.e., gender and age) and social bond (from strangers to family members) on yawn. The individual identity of the subjects (trigger and potential responder) was included as random factor. The social bond significantly predicted the occurrence of auditory yawn contagion, which was highest between friends and family members. A gender bias was also observed, with women responding most frequently to others’ yawns and men being responded to most frequently by others. These results confirm that social bond is per se one of the main drivers of the differences in yawn contagion rates between individuals in support of the EBH of yawn contagion.
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Affiliation(s)
- Ivan Norscia
- Department of Life Sciences and Systems Biology, School of Nature Sciences, University of Turin, Turin, Italy
| | - Anna Zanoli
- Department of Life Sciences and Systems Biology, School of Nature Sciences, University of Turin, Turin, Italy
| | - Marco Gamba
- Department of Life Sciences and Systems Biology, School of Nature Sciences, University of Turin, Turin, Italy
| | - Elisabetta Palagi
- Unit of Ethology, Department of Biology, University of Pisa, Pisa, Italy
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