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      Comparing Public Perceptions and Preventive Behaviors During the Early Phase of the COVID-19 Pandemic in Hong Kong and the United Kingdom: Cross-sectional Survey Study

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          Abstract

          Background

          Given the public health responses to previous respiratory disease pandemics, and in the absence of treatments and vaccines, the mitigation of the COVID-19 pandemic relies on population engagement in nonpharmaceutical interventions. This engagement is largely driven by risk perception, anxiety levels, and knowledge, as well as by historical exposure to disease outbreaks, government responses, and cultural factors.

          Objective

          The aim of this study is to compare psychobehavioral responses in Hong Kong and the United Kingdom during the early phase of the COVID-19 pandemic.

          Methods

          Comparable cross-sectional surveys were administered to adults in Hong Kong and the United Kingdom during the early phase of the epidemic in each setting. Explanatory variables included demographics, risk perception, knowledge of COVID-19, anxiety level, and preventive behaviors. Responses were weighted according to census data. Logistic regression models, including effect modification to quantify setting differences, were used to assess the association between the explanatory variables and the adoption of social distancing measures.

          Results

          Data from 3431 complete responses (Hong Kong, 1663; United Kingdom, 1768) were analyzed. Perceived severity of symptoms differed by setting, with weighted percentages of 96.8% for Hong Kong (1621/1663) and 19.9% for the United Kingdom (366/1768). A large proportion of respondents were abnormally or borderline anxious (Hong Kong: 1077/1603, 60.0%; United Kingdom: 812/1768, 46.5%) and regarded direct contact with infected individuals as the transmission route of COVID-19 (Hong Kong: 94.0%-98.5%; United Kingdom: 69.2%-93.5%; all percentages weighted), with Hong Kong identifying additional routes. Hong Kong reported high levels of adoption of various social distancing measures (Hong Kong: 32.6%-93.7%; United Kingdom: 17.6%-59.0%) and mask-wearing (Hong Kong: 98.8% (1647/1663); United Kingdom: 3.1% (53/1768)). The impact of perceived severity of symptoms and perceived ease of transmission of COVID-19 on the adoption of social distancing measures varied by setting. In Hong Kong, these factors had no impact, whereas in the United Kingdom, those who perceived their symptom severity as “high” were more likely to adopt social distancing (adjusted odds ratios [aORs] 1.58-3.01), and those who perceived transmission as “easy” were prone to adopt both general social distancing (aOR 2.00, 95% CI 1.57-2.55) and contact avoidance (aOR 1.80, 95% CI 1.41-2.30). The impact of anxiety on adopting social distancing did not vary by setting.

          Conclusions

          Our results suggest that health officials should ascertain baseline levels of risk perception and knowledge in populations, as well as prior sensitization to infectious disease outbreaks, during the development of mitigation strategies. Risk should be communicated through suitable media channels—and trust should be maintained—while early intervention remains the cornerstone of effective outbreak response.

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          Most cited references34

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          Demographic and attitudinal determinants of protective behaviours during a pandemic: A review

          Purpose. A new strain of H1N1 influenza, also known as swine flu was confirmed in the UK in May 2009 and has spread to over 100 countries around the world causing the World Health Organization to declare a global flu pandemic. The primary objectives of this review are to identify the key demographic and attitudinal determinants of three types of protective behaviour during a pandemic: preventive, avoidant, and management of illness behaviours, in order to describe conceptual frameworks in which to better understand these behaviours and to inform future communications and interventions in the current outbreak of swine flu and subsequent influenza pandemics. Methods. Web of Science and PubMed databases were searched for references to papers on severe acute respiratory syndrome, avian influenza/flu, H5N1, swine influenza/flu, H1N1, and pandemics. Forward searching of the identified references was also carried out. In addition, references were gleaned from an expert panel of the Behaviour and Communications sub‐group of the UK Scientific Pandemic Influenza Advisory Group. Papers were included if they reported associations between demographic factors, attitudes, and a behavioural measure (reported, intended, or actual behaviour). Results. Twenty‐six papers were identified that met the study inclusion criteria. The studies were of variable quality and most lacked an explicit theoretical framework. Most were cross‐sectional in design and therefore not predictive over time. The research shows that there are demographic differences in behaviour: being older, female and more educated, or non‐White, is associated with a higher chance of adopting the behaviours. There is evidence that greater levels of perceived susceptibility to and perceived severity of the diseases and greater belief in the effectiveness of recommended behaviours to protect against the disease are important predictors of behaviour. There is also evidence that greater levels of state anxiety and greater trust in authorities are associated with behaviour. Conclusions. The findings from this review can be broadly explained by theories of health behaviour. However, theoretically driven prospective studies are required to further clarify the relationship between demographic factors, attitudes, and behaviour. The findings suggest that intervention studies and communication strategies should focus on particular demographic groups and on raising levels of perceived threat of the pandemic disease and belief in the effectiveness of measures designed to protect against it.
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            Modelling the influence of human behaviour on the spread of infectious diseases: a review.

            Human behaviour plays an important role in the spread of infectious diseases, and understanding the influence of behaviour on the spread of diseases can be key to improving control efforts. While behavioural responses to the spread of a disease have often been reported anecdotally, there has been relatively little systematic investigation into how behavioural changes can affect disease dynamics. Mathematical models for the spread of infectious diseases are an important tool for investigating and quantifying such effects, not least because the spread of a disease among humans is not amenable to direct experimental study. Here, we review recent efforts to incorporate human behaviour into disease models, and propose that such models can be broadly classified according to the type and source of information which individuals are assumed to base their behaviour on, and according to the assumed effects of such behaviour. We highlight recent advances as well as gaps in our understanding of the interplay between infectious disease dynamics and human behaviour, and suggest what kind of data taking efforts would be helpful in filling these gaps.
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              The Hospital Anxiety And Depression Scale

              R Snaith (2003)
              There is a need to assess the contribution of mood disorder, especially anxiety and depression, in order to understand the experience of suffering in the setting of medical practice. Most physicians are aware of this aspect of the illness of their patients but many feel incompetent to provide the patient with reliable information. The Hospital Anxiety And Depression Scale, or HADS, was designed to provide a simple yet reliable tool for use in medical practice. The term 'hospital' in its title suggests that it is only valid in such a setting but many studies conducted throughout the world have confirmed that it is valid when used in community settings and primary care medical practice. It should be emphasised that self-assessment scales are only valid for screening purposes; definitive diagnosis must rest on the process of clinical examination.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J Med Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                March 2021
                8 March 2021
                8 March 2021
                : 23
                : 3
                : e23231
                Affiliations
                [1 ] MRC Centre for Global Infectious Disease Analysis and Abdul Latif Jameel Institute for Disease and Emergency Analytics (J-IDEA) School of Public Health Imperial College London United Kingdom
                [2 ] JC School of Public Health and Primary Care The Chinese University of Hong Kong Hong Kong Hong Kong
                [3 ] Stanley Ho Centre for Emerging Infectious Diseases The Chinese University of Hong Kong Hong Kong Hong Kong
                [4 ] Shenzhen Research Institute The Chinese University of Hong Kong Hong Kong Hong Kong
                [5 ] Patient Experience Research Centre School of Public Health Imperial College London London United Kingdom
                Author notes
                Corresponding Author: Kin On Kwok kkokwok@ 123456cuhk.edu.hk
                Author information
                https://orcid.org/0000-0003-0657-7911
                https://orcid.org/0000-0002-2804-5433
                https://orcid.org/0000-0003-4776-2769
                https://orcid.org/0000-0003-2594-8423
                https://orcid.org/0000-0001-8238-5036
                https://orcid.org/0000-0001-8083-3528
                https://orcid.org/0000-0001-8304-7389
                https://orcid.org/0000-0003-0934-6385
                Article
                v23i3e23231
                10.2196/23231
                7942393
                33539309
                46407cd8-b9c8-42a8-9815-fd604f4e8d9a
                ©Leigh Bowman, Kin On Kwok, Rozlyn Redd, Yuanyuan Yi, Helen Ward, Wan In Wei, Christina Atchison, Samuel Yeung-Shan Wong. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 08.03.2021.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 5 August 2020
                : 19 September 2020
                : 19 October 2020
                : 1 February 2021
                Categories
                Original Paper
                Original Paper

                Medicine
                covid-19,novel coronavirus,pandemic,behavioural response,risk perceptions,anxiety,comparative,hong kong,united kingdom

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