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3.
JMIR Public Health Surveill ; 7(4): e26780, 2021 04 05.
Article in English | MEDLINE | ID: covidwho-2141318

ABSTRACT

BACKGROUND: Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media. OBJECTIVE: This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases. METHODS: We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data collected. We investigated the relationship between the volume of tweets against mask-wearing and the daily volume of new COVID-19 cases using a Pearson correlation analysis between the two-time series. RESULTS: The results and analysis showed that social media could help identify important insights related to wearing masks. The results of topic mining identified 10 categories or themes of user concerns dominated by (1) constitutional rights and freedom of choice; (2) conspiracy theory, population control, and big pharma; and (3) fake news, fake numbers, and fake pandemic. Altogether, these three categories represent almost 65% of the volume of tweets against wearing masks. The relationship between the volume of tweets against wearing masks and newly reported COVID-19 cases depicted a strong correlation wherein the rise in the volume of negative tweets led the rise in the number of new cases by 9 days. CONCLUSIONS: These findings demonstrated the potential of mining social media for understanding the public discourse about public health issues such as wearing masks during the COVID-19 pandemic. The results emphasized the relationship between the discourse on social media and the potential impact on real events such as changing the course of the pandemic. Policy makers are advised to proactively address public perception and work on shaping this perception through raising awareness, debunking negative sentiments, and prioritizing early policy intervention toward the most prevalent topics.


Subject(s)
COVID-19/epidemiology , COVID-19/prevention & control , Masks , Public Opinion , Social Media/statistics & numerical data , Data Mining , Humans , Machine Learning , United States/epidemiology
4.
PLoS One ; 17(11): e0277394, 2022.
Article in English | MEDLINE | ID: covidwho-2119444

ABSTRACT

The COVID-19 pandemic has changed society and people's lives. The vaccination campaign started December 27th 2020 in Italy, together with most countries in the European Union. Social media platforms can offer relevant information about how citizens have experienced and perceived the availability of vaccines and the start of the vaccination campaign. This study aims to use machine learning methods to extract sentiments and topics relating to COVID-19 vaccination from Twitter. Between February and May 2021, we collected over 71,000 tweets containing vaccines-related keywords from Italian Twitter users. To get the dominant sentiment throughout the Italian population, spatial and temporal sentiment analysis was performed using VADER, highlighting sentiment fluctuations strongly influenced by news of vaccines' side effects. Additionally, we investigated the opinions of Italians with respect to different vaccine brands. As a result, 'Oxford-AstraZeneca' vaccine was the least appreciated among people. The application of the Dynamic Latent Dirichlet Allocation (DLDA) model revealed three fundamental topics, which remained stable over time: vaccination plan info, usefulness of vaccinating and concerns about vaccines (risks, side effects and safety). To the best of our current knowledge, this one the first study on Twitter to identify opinions about COVID-19 vaccination in Italy and their progression over the first months of the vaccination campaign. Our results can help policymakers and research communities track public attitudes towards COVID-19 vaccines and help them make decisions to promote the vaccination campaign.


Subject(s)
COVID-19 Vaccines , COVID-19 , Social Media , Humans , COVID-19/epidemiology , COVID-19/prevention & control , Natural Language Processing , Pandemics/prevention & control , Papillomavirus Vaccines , Public Opinion
5.
Int J Environ Res Public Health ; 19(22)2022 Nov 10.
Article in English | MEDLINE | ID: covidwho-2110079

ABSTRACT

BACKGROUND: During the outbreak of COVID-19, online public opinion related to the epidemic was rapidly generated and developed rapidly. If some online public opinions cannot be effectively responded to and guided, it will bring risks to social order. The government should understand how to use information on social media to grasp public demands, provide useful information in a timely manner and take countermeasures. Studying the formation mechanism of online public opinion during the outbreak can help the government make scientific decisions and improve risk management capabilities. METHODS: The research selects the public opinion information of online platforms represented by WeChat, online communities, Sina Weibo and search engines, involving 75 relevant texts (1 January to 31 March 2022). According to the grounded theory method, using the QSR NVivo12 qualitative research software, the collected network texts were successively researched using open coding, axial coding and theoretical coding. RESULTS: The structure of online public opinion during the COVID-19 epidemic was obtained. The operation mechanism of the online public opinion system about COVID-19 was mainly affected by the interaction of online public opinion objects, online public opinion subjects, online public opinion intermediaries and government forces. It was based on social facts and citizens' appeals as the starting point, subject behaviors and prevention and control measures as the focus, government's governance as macro-control and citizens' evaluation as the guide. CONCLUSIONS: Scientific analysis of online public opinion is an important tool to identify and manage risks and improve the quality of government activities. Online public opinion has the function of assisting government decision-making, and the government can identify the important information reflected in it, especially the mainstream public opinion, as a reference for decision-making. By taking effective measures and properly responding to citizens' reasonable demands, the government can prevent social risks and avoid new negative public opinions. Contributions: According to the characteristics of the basic model of online public opinion, this study provides risk mitigation suggestions for Chinese public sectors to use online public opinion, optimize epidemic prevention policies and formulate strategic measures.


Subject(s)
COVID-19 , Public Opinion , Humans , COVID-19/epidemiology , Grounded Theory , China/epidemiology , Risk Management
6.
J Telemed Telecare ; 28(10): 718-725, 2022 Dec.
Article in English | MEDLINE | ID: covidwho-2108472

ABSTRACT

While COVID-19 catalyzed the acceptance and use of telehealth, our understanding of how it is perceived by multi-stakeholders such as patients, clinicians, and health authorities is limited. Drawing on social media analytics, this research examines social media discourses and users' opinions about telehealth during the COVID-19 pandemic. It applies natural language processing and deep learning to explore word of mouth on telehealth with a contextualized focus on the COVID-19 pandemic. We conducted topic modeling, sentiment analysis, and emotion analysis (fearful, happy, sad, surprised, and angry emotions). The topic modeling analysis led to the identification of 18 topics, representing 6 themes of digital health service delivery, pandemic response, communication and promotion, government action, health service domains (e.g. mental health, cancer, aged care), as well as pharma and drug. The sentiment analysis revealed that while most opinions expressed in tweets were positive, the public expressed mostly negative opinions about certain aspects of COVID-19 such as lockdowns and cyberattacks. Emotion analysis of tweets showed a dominant pattern of fearful and sad emotions in particular topics. The results of this study that inductively emerged from our social media analysis can aid public health authorities and health professionals to address the concerns of telehealth users and improve their experiences.


Subject(s)
COVID-19 , Social Media , Telemedicine , Humans , Aged , COVID-19/epidemiology , Pandemics , Public Opinion , Communicable Disease Control
7.
Nature ; 611(7935): 332-345, 2022 11.
Article in English | MEDLINE | ID: covidwho-2106424

ABSTRACT

Despite notable scientific and medical advances, broader political, socioeconomic and behavioural factors continue to undercut the response to the COVID-19 pandemic1,2. Here we convened, as part of this Delphi study, a diverse, multidisciplinary panel of 386 academic, health, non-governmental organization, government and other experts in COVID-19 response from 112 countries and territories to recommend specific actions to end this persistent global threat to public health. The panel developed a set of 41 consensus statements and 57 recommendations to governments, health systems, industry and other key stakeholders across six domains: communication; health systems; vaccination; prevention; treatment and care; and inequities. In the wake of nearly three years of fragmented global and national responses, it is instructive to note that three of the highest-ranked recommendations call for the adoption of whole-of-society and whole-of-government approaches1, while maintaining proven prevention measures using a vaccines-plus approach2 that employs a range of public health and financial support measures to complement vaccination. Other recommendations with at least 99% combined agreement advise governments and other stakeholders to improve communication, rebuild public trust and engage communities3 in the management of pandemic responses. The findings of the study, which have been further endorsed by 184 organizations globally, include points of unanimous agreement, as well as six recommendations with >5% disagreement, that provide health and social policy actions to address inadequacies in the pandemic response and help to bring this public health threat to an end.


Subject(s)
COVID-19 , Delphi Technique , International Cooperation , Public Health , Humans , COVID-19/economics , COVID-19/epidemiology , COVID-19/prevention & control , Government , Pandemics/economics , Pandemics/prevention & control , Public Health/economics , Public Health/methods , Organizations , COVID-19 Vaccines , Communication , Health Education , Health Policy , Public Opinion
8.
Value Health ; 25(9): 1469-1479, 2022 09.
Article in English | MEDLINE | ID: covidwho-2084454

ABSTRACT

OBJECTIVES: This study aimed to review definitions of digital health and understand their relevance for health outcomes research. Four umbrella terms (digital health, electronic health, mobile health, and telehealth/telemedicine) were summarized in this article. METHODS: PubMed/MEDLINE, Embase, Cochrane Library, and EconLit were searched from January 2015 to May 2020 for systematic reviews containing key Medical Subject Headings terms for digital health (n = 38) and synonyms of "definition." Independent pairs of reviewers performed each stage of the review, with reconciliation by a third reviewer if required. A single reviewer consolidated each definition for consistency. We performed text analysis via word clouds and computed document frequency-and inverse corpus frequency scores. RESULTS: The search retrieved 2610 records with 545 articles (20.9%) taken forward for full-text review. Of these, 39.3% (214 of 545) were eligible for data extraction, of which 134 full-text articles were retained for this analysis containing 142 unique definitions of umbrella terms (digital health [n = 4], electronic health [n = 36], mobile health [n = 50], and telehealth/telemedicine [n = 52]). Seminal definitions exist but have increasingly been adapted over time and new definitions were created. Nevertheless, the most characteristic words extracted from the definitions via the text analyses still showed considerable overlap between the 4 umbrella terms. CONCLUSIONS: To focus evidence summaries for outcomes research purposes, umbrella terms should be accompanied by Medical Subject Headings terms reflecting population, intervention, comparator, outcome, timing, and setting. Ultimately a functional classification system is needed to create standardized terminology for digital health interventions denoting the domains of patient-level effects and outcomes.


Subject(s)
Telemedicine , Text Messaging , Humans , Outcome Assessment, Health Care , Public Opinion , Systematic Reviews as Topic
9.
Int J Environ Res Public Health ; 19(20)2022 Oct 14.
Article in English | MEDLINE | ID: covidwho-2071450

ABSTRACT

The COVID-19 pandemic has created unprecedented burdens on people's health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.


Subject(s)
COVID-19 , Social Media , Humans , COVID-19 Vaccines , Pandemics/prevention & control , COVID-19/epidemiology , COVID-19/prevention & control , Public Opinion , China/epidemiology
10.
PLoS One ; 17(10): e0275075, 2022.
Article in English | MEDLINE | ID: covidwho-2065130

ABSTRACT

To assess the levels of infection across communities during the coronavirus disease 2019 pandemic, researchers have measured severe acute respiratory syndrome coronavirus 2 RNA in feces dissolved in sewer water. This activity is colloquially known as sewer monitoring and is referred to as wastewater-based epidemiology in academic settings. Although global ethical principles have been described, sewer monitoring is unregulated for health privacy protection when used for public health surveillance in the United States. This study used Qualtrics XM, a national research panel provider, to recruit participants to answer an online survey. Respondents (N = 3,083) answered questions about their knowledge, perceptions of what is to be monitored, where monitoring should occur, and privacy concerns related to sewer monitoring as a public health surveillance tool. Furthermore, a privacy attitude questionnaire was used to assess the general privacy boundaries of respondents. Participants were more likely to support monitoring for diseases (92%), environmental toxins (92%), and terrorist threats (88%; e.g., anthrax). Two-third of the respondents endorsed no prohibition on location sampling scale (e.g., monitoring single residence to entire community was acceptable); the most common location category respondents wanted to prohibit sampling was at personal residences. Sewer monitoring is an emerging technology, and our study sheds light on perceptions that could benefit from educational programs in areas where public acceptance is comparatively lower. Respondents clearly communicated guard rails for sewer monitoring, and public opinion should inform future policy, application, and regulation measures.


Subject(s)
COVID-19 , Waste Water , COVID-19/epidemiology , Humans , Public Health , Public Opinion , RNA , United States , Water
11.
Sci Rep ; 12(1): 17095, 2022 Oct 12.
Article in English | MEDLINE | ID: covidwho-2062272

ABSTRACT

Social media platforms significantly increase general information about disease severity and inform preventive measures among community members. To identify public opinion through tweets on the subject of Covid-19 and investigate public sentiment in the country over the period. This article proposed a novel method for sentiment analysis of coronavirus-related tweets using bidirectional encoder representations from transformers (BERT) bi-directional long short-term memory (Bi-LSTM) ensemble learning model. The proposed approach consists of two stages. In the first stage, the BERT model gains the domain knowledge with Covid-19 data and fine-tunes with sentiment word dictionary. The second stage is the Bi-LSTM model, which is used to process the data in a bi-directional way with context sequence dependency preserving to process the data and classify the sentiment. Finally, the ensemble technique combines both models to classify the sentiment into positive and negative categories. The result obtained by the proposed method is better than the state-of-the-art methods. Moreover, the proposed model efficiently understands the public opinion on the Twitter platform, which can aid in formulating, monitoring and regulating public health policies during a pandemic.


Subject(s)
COVID-19 , Social Media , Attitude , COVID-19/epidemiology , Humans , Pandemics , Public Opinion
12.
Health Aff (Millwood) ; 41(10): 1513-1522, 2022 10.
Article in English | MEDLINE | ID: covidwho-2054398

ABSTRACT

The COVID-19 pandemic offers an opportunity to examine public opinion regarding the allocation of scarce medical resources. In this conjoint experiment on a nationally representative sample of US adults, we examined how a range of patient characteristics affect respondents' willingness to allocate a ventilator between two patients with equal likelihood of short-term survival and how this differs by respondents' attributes. Respondents were 5.5 percentage points less likely to allocate a ventilator to a patient with a disability than to a nondisabled patient. Disability bias was correlated with older age cohorts and higher education levels of respondents. Liberal and moderate respondents were more likely to give a ventilator to Black and Asian patients than to White patients. Conservatives were much less likely to allocate a ventilator to transgender patients than to cisgender patients. These findings demonstrate the importance of bias mitigation and civil rights enforcement in health policy making, especially under conditions of scarcity.


Subject(s)
COVID-19 , Adult , Humans , Pandemics , Public Opinion , Resource Allocation , Ventilators, Mechanical
13.
PLoS One ; 17(2): e0264618, 2022.
Article in English | MEDLINE | ID: covidwho-2054283

ABSTRACT

Shopping behaviour in response to extreme events is often characterized as "panic buying" which connotes irrationality and loss of control. However, "panic buying" has been criticized for attributing shopping behaviour to people's alleged psychological frailty while ignoring other psychological and structural factors that might be at play. We report a qualitative exploration of the experiences and understandings of shopping behaviour of members of the public at the onset of the COVID-19 pandemic. Through a thematic analysis of semi-structured interviews with 23 participants, we developed three themes. The first theme addresses people's understandings of "panic buying". When participants referred to "panic buying" they meant observed product shortages (rather than the underlying psychological processes that can lead to such behaviours), preparedness behaviours, or emotions such as fear and worry. The second theme focuses on the influence of the media and other people's behaviour in shaping subsequent shopping behaviours. The third theme addresses the meaningful motivations behind increased shopping, which participants described in terms of preparedness; some participants reported increased shopping behaviours as a response to other people stockpiling, to reduce their trips to supermarkets, or to prepare for product shortages and longer stays at home. Overall, despite frequently using the term 'panic', the irrationalist connotations of "panic buying" were largely absent from participants' accounts. Thus, "panic buying" is not a useful concept and should not be used as it constructs expected responses to threat as irrational or pathological. It can also facilitate such behaviours, creating a self-fulfilling prophecy.


Subject(s)
COVID-19 , Consumer Behavior , Hoarding/psychology , Panic , Public Opinion , Anxiety/psychology , Fear/psychology , Humans , Pandemics
14.
Sci Data ; 9(1): 583, 2022 09 23.
Article in English | MEDLINE | ID: covidwho-2042331

ABSTRACT

This study gathered evidence from Germany and the United States on public opinion towards fair distribution of COVID-19 vaccines across the world. Analytical Hierarchy Process and discrete choice experiments were used for this purpose. The sample is nationally representative of adults (aged 18 and above) for both countries using quotas on age, gender, education, state, and COVID-19 vaccination rates at the time of the fieldwork (25 May 2021 to 26 June 2021). Overall 1,003 responses in Germany and 1,000 in the United States were collected.


Subject(s)
COVID-19 Vaccines , COVID-19 , Adult , COVID-19/prevention & control , Germany , Humans , Public Opinion , United States , Vaccination
15.
Alcohol ; 103: 1-7, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-2035682

ABSTRACT

On November 19th, 2021, the annual Alcohol and Immunology Research Interest Group (AIRIG) meeting was held at Loyola University Chicago Health Sciences Campus in Maywood, Illinois. The 2021 meeting focused on how alcohol misuse is linked to immune system derangements, leading to tissue and organ damage, and how this research can be translated into improving treatment of alcohol-related disease. This meeting was divided into three plenary sessions: the first session focused on how alcohol misuse affects different parts of the immune system, the second session presented research on mechanisms of organ damage from alcohol misuse, and the final session highlighted research on potential therapeutic targets for treating alcohol-mediated tissue damage. Diverse areas of alcohol research were covered during the meeting, from alcohol's effect on pulmonary systems and neuroinflammation to epigenetic changes, senescence markers, and microvesicle particles. These presentations yielded a thoughtful discussion on how the findings can lead to therapeutic treatments for people suffering from alcohol-related diseases.


Subject(s)
Alcoholism , Alcoholism/genetics , Epigenesis, Genetic , Ethanol/adverse effects , Humans , Inflammation/genetics , Public Opinion
16.
PLoS One ; 17(9): e0274580, 2022.
Article in English | MEDLINE | ID: covidwho-2029796

ABSTRACT

Evidence from the early months of the COVID-19 pandemic in the U.S. indicated that the virus had vastly different effects across races, with black Americans faring worse on dimensions including illness, hospitalization and death. New data suggests that our understanding of the pandemic's racial inequities must be revised given the closing of the gap between black and white COVID-related mortality. Initial explanations for inequality in COVID-related outcomes concentrated on static factors-e.g., geography, urbanicity, segregation or age-structures-that are insufficient on their own to explain observed time-varying patterns in inequality. Drawing from a literature suggesting the relevance of political factors in explaining pandemic outcomes, we highlight the importance of political polarization-the partisan divide in pandemic-related policies and beliefs-that varies over time and across geographic units. Specifically, we investigate the role of polarization through two political factors, public opinion and state-level public health policies, using fine-grained data on disparities in public concern over COVID and in state containment/health policies to understand the changing pattern of inequality in mortality. We show that (1) apparent decreases in inequality are driven by increasing total deaths-mostly among white Americans-rather than decreasing mortality among black Americans (2) containment policies are associated with decreasing inequality, likely resulting from lower relative mortality among Blacks (3) as the partisan disparity in Americans who were "unconcerned" about COVID increased, racial inequality in COVID mortality decreased, generating the appearance of greater equality consistent with a "race to the bottom'' explanation as overall deaths increased and substantively swamping the effects of containment policies.


Subject(s)
COVID-19 , Public Opinion , Humans , Pandemics , Politics , United States/epidemiology , Whites
17.
PLoS One ; 17(4): e0264134, 2022.
Article in English | MEDLINE | ID: covidwho-2021605

ABSTRACT

BACKGROUND: Confidence in the central UK Government has declined since the beginning of the COVID-19 pandemic, and while this may be linked to specific government actions to curb the spread of the virus, understanding is still incomplete. Examining public opinion is important, as research suggests that low confidence in government increases the extent of non-compliance with infection-dampening rules (for instance, social distancing); however, the detailed reasons for this association are still unclear. METHODS: To understand public opinion on the central UK government during the first phase of the COVID-19 pandemic, we used structural topic modeling, a text mining technique, to extract themes from over 4000 free-text survey responses, collected between 14 October and 26 November 2020. RESULTS: We identified eleven topics, among which were topics related to perceived government corruption and cronyism, complaints about inconsistency in rules and messaging, lack of clear planning, and lack of openness and transparency. Participants reported that elements of the government's approach had made it difficult to comply with guidelines (e.g., changing rules) or were having impacts on mental wellbeing (e.g., inability to plan for the future). CONCLUSIONS: Results suggested that consistent, transparent communication and messaging from the government is critical to improving compliance with measures to contain the virus, as well as protecting mental health during health emergencies.


Subject(s)
COVID-19 , COVID-19/epidemiology , COVID-19/prevention & control , Government , Humans , Pandemics/prevention & control , Public Opinion , SARS-CoV-2 , United Kingdom/epidemiology
18.
J Environ Public Health ; 2022: 6294436, 2022.
Article in English | MEDLINE | ID: covidwho-2020519

ABSTRACT

Climate change is a serious threat to humankind. As broad public participation is required in climate change mitigation efforts, it is critical to understand how the public talk about climate change on social media. This study sets out to increase the understanding of Chinese public awareness of climate change, as well as explore the potential and limitations of social media for public engagement on climate change issues. It examines the Chinese public's discussion about climate change on social media Weibo during the last six years through data mining and text analysis. The analyses include volume analysis, keyword extraction, topic modeling, and sentiment analysis. The results indicate three main aspects of public awareness and concern regarding climate change. First, public awareness of climate change is growing in China. Second, the sentiment analysis shows that the general sentiment toward climate change is becoming more positive over time. Third, based on keyword extraction and topic modeling, the discussion on climate change shows a top-down perspective, an optimistic economic perspective, and a preference for celebrity content. The study provides a comprehensive picture of Chinese social media users' views on climate change issues, based on large-scale research data. It contributes to a better understanding of what Chinese people think about climate change on social media generally. These findings may provide government and environmental organizations with valuable insights for better climate change campaigns on social media.


Subject(s)
Social Media , China , Climate Change , Data Mining/methods , Humans , Public Opinion
20.
PLoS One ; 17(8): e0273000, 2022.
Article in English | MEDLINE | ID: covidwho-1993508

ABSTRACT

BACKGROUND: There is evidence that perceived urgency of medical complaints is associated with emergency care utilization. Patients' perception of urgency can differ from physicians' assessment. This study explored public perceptions of urgency of severe cases of COVID-19 and inflammatory gastrointestinal disease and analyzed variations in perceptions of urgency by characteristics of the afflicted person in the vignettes and sociodemographic characteristics of respondents. METHODS: Vignettes with severe symptoms of either inflammatory gastrointestinal disease or COVID-19 with comparable urgency of treatment were used in a telephone survey in Germany (N = 1,207). Besides disease, the vignettes varied in terms of sex, age (child, middle-aged person, old person) and daytime (Tuesday morning, Tuesday evening). Respondents were asked to rate the urgency of the reported symptoms with four items. A sum scale was computed. Variations in perceptions of urgency according to vignette characteristics and sociodemographic characteristics of the respondents (sex, age, educational level, migration background, children (yes/no) and personal affliction) were analyzed using a linear regression model. RESULTS: In terms of vignette characteristics, multivariate analysis showed a lower estimated urgency for males, as well as for the middle-aged and aged persons, compared to the child vignettes, and for COVID-19, compared to inflammatory gastrointestinal disease. Regarding the characteristics of the respondents, estimated urgency increased with age and was lower among respondents, who were previously affected by the symptoms themselves. CONCLUSION: Although urgency in the vignettes was comparable, variations in estimated urgency by age and sex of the afflicted person and the described disease as well as age and personal affliction of the respondents were identified. This could result in an inadequate health care service utilization. Therefore, variations in public perceptions of urgency should be considered in the planning of public campaigns on adequate health care services utilization.


Subject(s)
COVID-19 , Gastrointestinal Diseases , Aged , Child , Gastrointestinal Diseases/epidemiology , Germany/epidemiology , Humans , Male , Middle Aged , Public Opinion , Surveys and Questionnaires
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