Social mixing and risk exposures for SARS-CoV-2 infections in elderly persons
AIMS OF THE STUDY
During the transitional phase between the two pandemic waves of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), infection rates were temporarily rising among younger persons only. However, following a temporal delay infections started to expand to older age groups. A comprehensive understanding of such transmission dynamics will be key for managing the pandemic in the time to come and to anticipate future developments. The present study thus extends the scope of previous SARS-CoV-2-related research in Switzerland by contributing to deeper insight into the potential impact of “social mixing” of different age groups on the spread of SARS-CoV-2 infections.
The present study examined persons aged 65 years and older with respect to possible SARS-CoV-2 exposure risks using longitudinal panel data from the Swiss COVID-19 Social Monitor. The study used data from two assessments (survey “May” and survey “August”). Survey “May” took place shortly after the release of the lockdown in Switzerland. Survey “August” was conducted in mid-August. To identify at-risk elderly persons, we conducted a combined factor/k-means clustering analysis of the survey data assessed in August in order to examine different patterns of adherence to recommended preventive measures.
In summary, 270 (survey “May”) and 256 (survey “August”) persons aged 65 years and older were analysed for the present study. Adherence to established preventive measures was similar across the two surveys, whereas adherence pertaining to social contacts decreased substantially from survey “May” to survey “August”. The combined factor/k-means clustering analysis to identify at-risk elderly individuals yielded four distinct groups with regard to different patterns of adherence to recommended preventive measures: a larger group of individuals with many social contacts but high self-reported adherence to preventive measures (n = 86); a small group with many social contacts and overall lower adherence (n = 26); a group with comparatively few contacts and few social activities (n = 66); and a group which differed from the latter through fewer contacts but more social activities (n = 78). Sociodemographic characteristics and risk perception with regard to SARS-CoV-2 infections among the four groups did not differ in a relevant way across the four groups.
Although many elderly persons continued to follow the recommended preventive measures during the transitional phase between the two pandemic waves, social mixing with younger persons constitutes a way for transmission of infections across age groups. Pandemic containment among all age groups thus remains essential to protect vulnerable populations, including the elderly.
- Bundesamt für Gesundheit (BAG). Neues Coronavirus: Situation Schweiz. Available from: https://www.bag.admin.ch/bag/de/home/krankheiten/ausbrueche-epidemien-pandemien/aktuelle-ausbrueche-epidemien/novel-cov/situation-schweiz-und-international.html.
- Bundesamt für Gesundheit. COVID-19 - Situation Schweiz: Verteilung nach Kanton, Alter und Geschlecht. Available from: https://covid-19-schweiz.bagapps.ch/de-1.html [accessed 2020 September 23].
- Harris JE. Data from the COVID-19 epidemic in Florida suggest that younger cohorts have been transmitting their infections to less socially mobile older adults. Rev Econ Househ. 2020;1–19. Online ahead of print. doi:.https://doi.org/10.1007/s11150-020-09496-w
- McMichael TM, Currie DW, Clark S, Pogosjans S, Kay M, Schwartz NG, et al.; Public Health–Seattle and King County, EvergreenHealth, and CDC COVID-19 Investigation Team. Epidemiology of Covid-19 in a Long-Term Care Facility in King County, Washington. N Engl J Med. 2020;382(21):2005–11. doi:.https://doi.org/10.1056/NEJMoa2005412
- Patel MC, Chaisson LH, Borgetti S, Burdsall D, Chugh RK, Hoff CR, et al. Asymptomatic SARS-CoV-2 infection and COVID-19 mortality during an outbreak investigation in a skilled nursing facility. Clin Infect Dis. 2020:ciaa763. doi:.https://doi.org/10.1093/cid/ciaa763
- Britton T, Ball F, Trapman P. A mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2. Science. 2020;369(6505):846–9. doi:.https://doi.org/10.1126/science.abc6810
- Ajrouch KJ, Blandon AY, Antonucci TC. Social networks among men and women: the effects of age and socioeconomic status. J Gerontol B Psychol Sci Soc Sci. 2005;60(6):S311–7. doi:.https://doi.org/10.1093/geronb/60.6.S311
- Moser A, Carlander M, Wieser S, Hämmig O, Puhan MA, Höglinger M. The COVID-19 Social Monitor longitudinal online panel: Real-time monitoring of social and public health consequences of the COVID-19 emergency in Switzerland. PLoS One. 2020;15(11):e0242129. doi:.https://doi.org/10.1371/journal.pone.0242129
- Howard MC. A Review of Exploratory Factor Analysis Decisions and Overview of Current Practices: What We Are Doing and How Can We Improve? Int J Hum Comput Interact. 2016;32(1):51–62. doi:.https://doi.org/10.1080/10447318.2015.1087664
- Jarvis CI, Van Zandvoort K, Gimma A, Prem K, Klepac P, Rubin GJ, et al.; CMMID COVID-19 working group. Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK. BMC Med. 2020;18(1):124. doi:.https://doi.org/10.1186/s12916-020-01597-8
- Latsuzbaia A, Herold M, Bertemes J-P, Mossong J. Evolving social contact patterns during the COVID-19 crisis in Luxembourg. PLoS One. 2020;15(8):e0237128. doi:.https://doi.org/10.1371/journal.pone.0237128
- von Wyl V, Höglinger M, Sieber C, Kaufmann M, Moser A, et al. Are COVID-19 proximity tracing apps working under real-world conditions? Indicator development and assessment of drivers for app (non-)use. Epidemiology. 2020. (Preprint). doi:.https://doi.org/10.1101/2020.08.29.20184382