A regression discontinuity analysis of the social distancing recommendations for older adults in Sweden during COVID-19
Year: 2022
Type: article
Study design: Observational Study
Abstract: BACKGROUND: This article investigates the impact of a non-mandatory and age-specific social distancing recommendation on isolation behaviours and disease outcomes in Sweden during the first wave of the coronavirus disease 2019 (COVID-19) pandemic (March to July 2020). The policy stated that people aged 70 years or older should avoid crowded places and contact with people outside the household. METHODS: We used a regression discontinuity design-in combination with self-reported isolation data from COVID Symptom Study Sweden (n = 96 053; age range: 39-79 years) and national register data (age range: 39-100+ years) on severe COVID-19 disease (hospitalization or death, n = 21 804) and confirmed cases (n = 48 984)-to estimate the effects of the policy. RESULTS: Our primary analyses showed a sharp drop in the weekly number of visits to crowded places (-13%) and severe COVID-19 cases (-16%) at the 70-year threshold. These results imply that the age-specific recommendations prevented approximately 1800-2700 severe COVID-19 cases, depending on model specification. CONCLUSIONS: It seems that the non-mandatory, age-specific recommendations helped control COVID-19 disease during the first wave of the pandemic in Sweden, as opposed to not implementing a social distancing policy aimed at older adults. Our study provides empirical data on how populations may react to non-mandatory, age-specific social distancing policies in the face of a novel virus.
Authors: Carl Bonander, Debora Stranges, Johanna Gustavsson, Matilda Almgren, Malin Inghammar, Mahnaz Moghaddassi, Anton Nilsson, Joan Capdevila Pujol, Claire Steves, Paul W Franks, Maria F Gomez, Tove Fall, Jonas Björk, COVID Symptom Study Sweden, Andrew T Chan, Sébastien Ourselin, Tim D Spector, Jonathan Wolf, Beatrice Kennedy, Hugo Fitipaldi, Ulf Hammar, Marlena Maziarz, Neli Tsereteli, Nikolay Oskolkov, Georgios Varotsis, Lampros Spiliopoulos
Language: English
Subfield: Modeling and Simulation
Field: Mathematics
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
Funders: Massachusetts General Hospital, Myndigheten för Samhällsskydd och Beredskap, King's College London, European Commission, Stiftelsen för Strategisk Forskning, Lunds Universitet, Hjärt-Lungfonden, Vetenskapsrådet, Göteborgs Universitet, Forskningsrådet om Hälsa, Arbetsliv och Välfärd, Uppsala Universitet, Engineering and Physical Sciences Research Council
Awards: G1191580964, G1865863944, G2004031785, Artificially Intelligent use of Registers (AIR Lund), Social Factors in Incidence And survival from childhood cancer (SOFIA), G263730981, G2725911048, Genome profiling, personalized medicine and cardiometabolic disease prevention, The CIVIC Project: A Sustainable Platform for COVID-19 syndromic-surveillance via Health, Deprivation and Mass Loyalty-Card Datasets, G3294204648, COVID-19 Vaccine Effectiveness, Responses and Safety (COVERS) Sweden: Clinical and epidemiological studies with special focus on vulnerable groups, Selective participation and loss to follow-up in population-based studies: novel approaches for dealing with unmeasured causes of selection, G3813181986, G3884363820, G453007591, G5268837257, G5468194863, G6373074959, G7786645318
OpenAlex ID: W4291250607