Age Differences in COVID-19 Risk Perceptions and Mental Health: Evidence From a National U.S. Survey Conducted in March 2020

Wändi Bruine de Bruin, Wändi Bruine de Bruin

Abstract

Objectives: Theories of aging posit that older adult age is associated with less negative emotions, but few studies have examined age differences at times of novel challenges. As COVID-19 spread in the United States, this study therefore aimed to examine age differences in risk perceptions, anxiety, and depression.

Method: In March 2020, a nationally representative address-based sample of 6,666 U.S. adults assessed their perceived risk of getting COVID-19, dying if getting it, getting quarantined, losing their job (if currently working), and running out of money. They completed a mental health assessment for anxiety and depression. Demographic variables and precrisis depression diagnosis had previously been reported.

Results: In regression analyses controlling for demographic variables and survey date, older adult age was associated with perceiving larger risks of dying if getting COVID-19, but with perceiving less risk of getting COVID-19, getting quarantined, or running out of money, as well as less depression and anxiety. Findings held after additionally controlling for precrisis reports of depression diagnosis.

Discussion: With the exception of perceived infection-fatality risk, U.S. adults who were relatively older appeared to have a more optimistic outlook and better mental health during the early stages of the pandemic. Interventions may be needed to help people of all ages maintain realistic perceptions of the risks, while also managing depression and anxiety during the COVID-19 crisis. Implications for risk communication and mental health interventions are discussed.

Keywords: Anxiety; Depression; Risk perception.

© The Author(s) 2020. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Figures

Figure 1.
Figure 1.
Age differences in risk perceptions. Note. Age groups were computed for presentation purposes only. The reported analyses treated age as a continuous variable. For all risks except job loss: N = 874 for age group <30, N = 1,630 for age group 30–39, N = 1,045 for age group 40–49, N = 1,102 for age group 50–59, N = 1,199 for age group 60–69, N = 816 for age group ≥70. For job loss: N = 597 for age group <30, N = 1283 for age group 30–39, N = 811 for age group 40–49, N = 788 for age group 50–59, N = 509 for age group 60–69, N = 131 for age group ≥70.
Figure 2.
Figure 2.
Age differences in warning signs for depression and anxiety disorder. Note. Age groups were computed for presentation purposes only. The reported analyses treated age as a continuous variable. Warning signs of depression and anxiety disorder referred to scores of ≥6 on the 4-item Patient Health Questionnaire (PHQ-4) and warning signs of either depression or anxiety disorder referred to scores of ≥3 on PHQ-4 subscales (Kroenke et al., 2009; Löwe et al., 2010). N = 874 for age group <30, N = 1,630 for age group 30–39, N = 1,045 for age group 40–49, N = 1,102 for age group 50–59, N = 1,199 for age group 60–69, N = 816 for age group ≥70.

References

    1. Alattar L., Messel M., & Rogofsky C (2018). An introduction to the understanding American study internet panel. Social Security Bulletin, 78, 13–28. Retrieved from .
    1. Bruine de Bruin W., & Bennett D. (in press). Relationships between initial COVID-19 risk perceptions and protective health behaviors: A national survey. American Journal of Preventive Medicine. doi:10.1016/j.amepre.2020.05.001
    1. Bruine de Bruin W., & Carman K. G (2018). Measuring subjective probabilities: The effect of response mode on the use of focal responses, validity, and respondents’ evaluations. Risk Analysis, 38, 2128–2143. doi:10.1111/risa.13138
    1. Carstensen L. L. (2006). The influence of a sense of time on human development. Science (New York, N.Y.), 312(5782), 1913–1915. doi:10.1126/science.1127488
    1. Carstensen L. L., Pasupathi M., Mayr U., & Nesselroade J. R (2000). Emotional experience in everyday life across the adult life span. Journal of Personality and Social Psychology, 79(4), 644–655. doi:10.1037//0022-3514.79.4.644
    1. Centers for Disease Control and Prevention (2014). Crisis and emergency risk communication Retrieved May 17, 2020, from
    1. Charles S. T. (2010). Strength and vulnerability integration: A model of emotional well-being across adulthood. Psychological Bulletin, 136(6), 1068–1091. doi:10.1037/a0021232
    1. Firth J., Torous J., Nicholas J., Carney R., Pratap A., Rosenbaum S., & Sarris J (2017). The efficacy of smartphone-based mental health interventions for depressive symptoms: A meta-analysis of randomized controlled trials. World Psychiatry, 16(3), 287–298. doi:10.1002/wps.20472
    1. García-Lizana F., & Muñoz-Mayorga I (2010). Telemedicine for depression: A systematic review. Perspectives in Psychiatric Care, 46(2), 119–126. doi:10.1111/j.1744-6163.2010.00247.x
    1. Grist R., & Cavanagh K (2013). Computerised cognitive behavioural therapy for common mental health disorders, what works, for whom under what circumstances? A systematic review and meta-analysis. Journal of Contemporary Psychotherapy, 43, 243–251. doi:10.1007/s10879-013-9243-y
    1. Kennedy C., Hatley N., Lau A., Mercer A., Keeter S., Ferno J., & Asare-Marfo D (2020). Assessing the risks to online polls from bogus respondents Pew Research Center; Retrieved May 11, 2020, from
    1. Kobbeltved T., Brun W., Johnsen B. H., & Eid J (2005). Risk as feelings or risk and feelings? A cross-lagged panel analysis. Journal of Risk Research, 8, 417–437. doi:10.1080/1366987042000315519
    1. Kroenke K., Spitzer R. L., Williams J. B. W., & Löwe B (2009). An ultra-brief screening scale for anxiety and depression. Psychosomatics, 50, 613–621. doi:10.1016/S0033-3182(09)70864-3
    1. Liu S., Yung L., Zhang C., Xiang Y.-T, Liu Z., Hu S., & Zhang B (2020). Online mental health services in China during the COVID-19 outbreak. The Lancet: Psychiatry, 7, PE17–PE18. doi:10.1016/S2215-0366(20)30077-8
    1. Löwe B., Wahl I., Rose M., Spitzer C., Glaesmer H., Wingenfeld K., . . . Brähler E (2010). A 4-item measure of depression and anxiety: Validation and standardization of the Patient Health Questionnaire-4 (PHQ-4) in the general population. Journal of Affective Disorders, 122, 86–95. doi:10.1016/j.jad.2009.06.019
    1. Neubauer A. B., Smyth J. M., & Sliwinski M. J (2019). Age differences in proactive coping with minor hassles in daily life. The Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 74(1), 7–16. doi:10.1093/geronb/gby061
    1. Novel Coronavirus Pneumonia Emergency Response Epidemiology Team (2020). The epidemiological characteristics of an outbreak of 2019 novel coronavirus diseases (COVID-19) — China, 2020. China CDC Weekly, 2, 113–122. doi:10.46234/ccdcw2020.032
    1. Parker K., & Stepler R (2017). As U.S. marriage rate hovers at 50%, education gap in marital status widens Pew Research Center; Retrieved May 11, 2020, from
    1. Prina A. M., Ferri C. P., Guerra M., Brayne C., & Prince M (2011). Prevalence of anxiety and its correlates among older adults in Latin America, India, and China: Cross-cultural study. The British Journal of Psychiatry, 199, 485–491. doi:10.1192/bjp.bp.110.083915
    1. Qiu J., Shen B., Zhao M., Xie B., & Xu Y (2020). A nationwide survey of psychological distress among Chinese people in the COVID-19 epidemic: Implications and policy reminders. BMJ: General Psychiatry, 33, 1–3. doi:10.1136/gpsych-2020-100213
    1. Scott S. B., Poulin M. J., & Silver R. C (2013). A lifespan perspective on terrorism: Age differences in trajectories of response to 9/11. Developmental Psychology, 49(5), 986–998. doi:10.1037/a0028916
    1. Shepperd J. A., Waters E., Weinstein N. D., & Klein W. M (2015). A primer on unrealistic optimism. Current Directions in Psychological Science, 24(3), 232–237. doi:10.1177/0963721414568341
    1. Tourangeau R., Conrad F. G., & Couper M. P (2013). The science of web surveys. New York, NY: Oxford University Press.
    1. Understanding America Study Recruitment Protocol (2019). USC Dornsife Center for Economic and Social Research Retrieved May 11, 2020, from
    1. United States Census Bureau (2018). QuickFacts United States Retrieved May 11, 2020, from
    1. Valliant R., Dever J. A., & Kreuter F (2013). Practical tools for designing and weighting survey samples. New York, NY: Springer.
    1. Wang C., Pan R., Wan X., Tan Y., Xu L., Ho C. S., & Ho R. C (2020). Immediate psychological responses and associated factors during the initial stage of the 2019 Coronavirus disease (COVID-19) epidemic among the general population in China. International Journal of Public Health Research and Public Health, 17, 1–25. doi:10.3390/ijerph17051729
    1. Witte K., & Allen M (2000). A meta-analysis of fear appeals: Implications for effective public health campaigns. Health Education & Behavior, 27(5), 591–615. doi:10.1177/109019810002700506

Source: PubMed

3
Se inscrever