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      One in Three Luxembourg Residents Report their Mental Health Declined during the COVID-19 Crisis

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          Abstract

          COVID-19 has had unprecedented effects on people around the world, causing even the best performing communities to live in uncertainty for the future. How are people coping? We – the general public, the academic community, and policy makers – need answers. To that end we analyse novel data for Luxembourg, finding one third of residents report their mental health declined during lockdown and young adults (ages 18–44) fared the worst. The most important contributors observed are physical health, income, and employment characteristics, such as working from home, which people seemed to enjoy. To limit collateral damage on mental health, various tools are available, which we briefly discuss.

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          Prospect Theory: An Analysis of Decision under Risk

          Daniel Kahneman, Amos Tversky (1979)
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            Estimating the effects of non-pharmaceutical interventions on COVID-19 in Europe

            Seth Flaxman, Swapnil Mishra, Axel Gandy … (2020)
            Following the detection of the new coronavirus1 severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its spread outside of China, Europe has experienced large epidemics of coronavirus disease 2019 (COVID-19). In response, many European countries have implemented non-pharmaceutical interventions, such as the closure of schools and national lockdowns. Here we study the effect of major interventions across 11 European countries for the period from the start of the COVID-19 epidemics in February 2020 until 4 May 2020, when lockdowns started to be lifted. Our model calculates backwards from observed deaths to estimate transmission that occurred several weeks previously, allowing for the time lag between infection and death. We use partial pooling of information between countries, with both individual and shared effects on the time-varying reproduction number (Rt). Pooling allows for more information to be used, helps to overcome idiosyncrasies in the data and enables more-timely estimates. Our model relies on fixed estimates of some epidemiological parameters (such as the infection fatality rate), does not include importation or subnational variation and assumes that changes in Rt are an immediate response to interventions rather than gradual changes in behaviour. Amidst the ongoing pandemic, we rely on death data that are incomplete, show systematic biases in reporting and are subject to future consolidation. We estimate that-for all of the countries we consider here-current interventions have been sufficient to drive Rt below 1 (probability Rt < 1.0 is greater than 99%) and achieve control of the epidemic. We estimate that across all 11 countries combined, between 12 and 15 million individuals were infected with SARS-CoV-2 up to 4 May 2020, representing between 3.2% and 4.0% of the population. Our results show that major non-pharmaceutical interventions-and lockdowns in particular-have had a large effect on reducing transmission. Continued intervention should be considered to keep transmission of SARS-CoV-2 under control.
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              The effect of human mobility and control measures on the COVID-19 epidemic in China

              Moritz Kraemer, Chia-Hung Yang, Bernardo Gutiérrez … (2020)
              The ongoing COVID-19 outbreak expanded rapidly throughout China. Major behavioral, clinical, and state interventions have been undertaken to mitigate the epidemic and prevent the persistence of the virus in human populations in China and worldwide. It remains unclear how these unprecedented interventions, including travel restrictions, affected COVID-19 spread in China. We use real-time mobility data from Wuhan and detailed case data including travel history to elucidate the role of case importation on transmission in cities across China and ascertain the impact of control measures. Early on, the spatial distribution of COVID-19 cases in China was explained well by human mobility data. Following the implementation of control measures, this correlation dropped and growth rates became negative in most locations, although shifts in the demographics of reported cases were still indicative of local chains of transmission outside Wuhan. This study shows that the drastic control measures implemented in China substantially mitigated the spread of COVID-19.
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                Author and article information

                Contributors
                Kelsey J. O’Connor:
                ORCID: https://orcid.org/0000-0002-1413-3619
                Kelsey.OConnor@statec.etat.lu
                Journal
                Journal ID (iso-abbrev): Int. Journal of Com. WB
                Title: International Journal of Community Well-Being
                Publisher: Springer International Publishing (Cham )
                ISSN (Print): 2524-5295
                ISSN (Electronic): 2524-5309
                Publication date (Electronic): 18 November 2020
                Pages: 1-7
                Affiliations
                STATEC Research (National Institute of Statistics and Economic Studies), Luxembourg, Luxembourg
                Author information
                https://orcid.org/0000-0002-1413-3619
                Article
                Publisher ID: 93
                DOI: 10.1007/s42413-020-00093-4
                PMC ID: 7672687
                PubMed ID: 34723111
                SO-VID: 3be1125f-d076-43bb-a2dc-9f527392b44a
                Copyright © © Springer Nature Switzerland AG 2020
                License:

                This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

                History
                Date received : 17 July 2020
                Date accepted : 22 October 2020
                Categories
                Subject: Commentary

                Keywords: covid-19,mental health,public policy,social distancing,lockdown
                Data availability:
                Keywords: covid-19, mental health, public policy, social distancing, lockdown

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