Warning “Don’t spread” vs. “Don’t be a spreader” to prevent the COVID-19 pandemic

Yonemitsu, Fumiya; Ikeda, Ayumi; Yoshimura, Naoto; Takashima, Kaito; Mori, Yuichi; Sasaki, Kyoshiro; Qian, Kun; Yamada, Yoshio

doi:10.31234/osf.io/u4z3e

Cited by 6 publications

(4 citation statements)

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“…To address motivation, messaging trials have explored different strategies to increase intentions to follow COVID-19 prevention advice, e.g. framing the behaviour as helping the community rather than avoiding individual risk 16 , or testing identity-based messages such as "don't be a spreader" 17 . However, emerging research in this area has neglected testing as a key behavioural outcome for COVID-19 prevention.…”

Section: Introductionmentioning

confidence: 99%

Behavioural barriers to COVID-19 testing in Australia: Two national surveys to identify barriers and estimate prevalence by health literacy level

Bonner

Batcup

Ayre

et al. 2021

Preprint

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Background: COVID-19 testing and contact tracing has been crucial in Australia's prevention strategy. However, testing for COVID-19 is far from optimal, and behavioural barriers are unknown. Study 1 aimed to identify the range of barriers to testing. Study 2 aimed to estimate prevalence in a nationally relevant sample to target interventions. Methods: Study 1: National longitudinal COVID-19 survey from April-November 2020. Testing barriers were included in the June survey (n=1369). Open responses were coded using the COM-B framework (capability-opportunity-motivation). Study 2: Barriers from Study 1 were presented to a new nationally representative sample in November to estimate prevalence (n=2869). Barrier prevalence was analysed by health literacy level using Chi square tests. Results: Study 1: 49% strongly agreed to get tested for symptoms, and 69% would self-isolate. Concern about pain was the top barrier from a provided list (11%), but 32 additional barriers were identified from open responses and coded to the COM-B framework. Study 2: The most prevalent barriers were motivation issues (e.g. don't believe symptoms are COVID-19: 28%, few local cases: 18%). Capability issues were also common (e.g. not sure symptoms are bad enough: 19%, not sure whether symptoms need testing: 15%). Many barriers were more prevalent amongst people with low compared to high health literacy, including motivation (preference to self isolate: 21% vs 12%, pain: 15% vs 9%) and capability (not sure symptom needs testing: 12% vs 8%, not sure how to test:11% vs 4%). Conclusion: Even in a health system with free and widespread access to COVID-19 testing, motivation and capability barriers were prevalent issues, particularly for people with lower health literacy. This study highlights the important of diagnosing behaviour barriers to target public health interventions for COVID-19 and future pandemics.

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Section: Introductionmentioning

confidence: 99%

Behavioural barriers to COVID-19 testing in Australia: Two national surveys to identify barriers and estimate prevalence by health literacy level

Bonner

Batcup

Ayre

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Messaging trials have tested different strategies to improve intentions to follow COVID-19 prevention advice, for example framing the motivation as helping the community rather than avoiding individual risk 14 , or testing identity-based messages such as “don ‘t be a spreader” 15 . However, emerging research in this area is generally not peer reviewed and has neglected testing as a key behavioural outcome for COVID-19 prevention.…”

Section: Introductionmentioning

confidence: 99%

Behavioural barriers to COVID-19 testing in Australia

Bonner

Batcup

Ayre

et al. 2020

Preprint

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Background: The current suppression strategy for COVID-19 in Australia is dependent on people getting tested and self-isolating while they have COVID-19 symptoms. However, there is very little research on the behaviours and behavioural barriers involved in getting tested, both in Australia and worldwide, despite there being some evidence that these barriers do exist. Methods: The Sydney Health Literacy Lab (SHeLL) has been conducting a national longitudinal survey in Australia since April 2020. A list of testing barriers was included in Wave 3 in June 2020 (n=1369), along with intentions to test and self-isolate if symptomatic. Open responses were also collected. The test barriers identified were categorised using the COM-B framework. Results: Only 49% of people strongly agreed they would get tested if they had COVID-19 symptoms, but most people agreed to some extent that they would get tested (96%). The most common barriers selected from the list provided were that testing is painful (11%), not knowing how to get tested (7%), and worry about getting infected at the testing centre (5%). Many participants (10%) indicated other reasons, and open responses included many additional barriers to testing than those provided in the initial list. These covered all components of the COM-B model. Conclusion: We identified a wide range of barriers using both quantitative and qualitative methods, which need to be addressed in order to increase COVID-19 testing behaviour.

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“…41 Government promotions have framed the motivation as helping the community rather than avoiding individual risk, 42 or using identity-based messages such as "don't be a spreader". 43 However, those living out of home with more secure financial footing were the least likely to have COVID tests, as indicated by the significant negative relationship between quintile 4 IRSAD and COVID testing. This could reflect possible instability in their employment position and unwillingness to risk calling in sick to isolate if testing positive.…”

Section: Discussionmentioning

confidence: 99%

Predictors of COVID testing among Australian youth: Insights from the Longitudinal Study of Australian Children

Islam

Chadwick

Martiniuk

2021

Preprint

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BackgroundTesting has played a crucial role in reducing the spread of COVID. Although COVID symptoms tend to be less severe in children and adolescents, a key concern is young people’s role in the transmission of the virus given their highly social lifestyles. In this study, we aimed to identify the predictors associated with COVID testing in Australian youth using data from the Longitudinal Study of Australian Children (LSAC).MethodsWe used the latest wave 9C1 of the LSAC, where data were collected from 16–21-year-old Australians via an online survey between October and December 2021. In total, 2291 Australian youths responded to the questions about COVID testing and COVID symptom severity. Data was stratified by living with/without parents, and bivariate and logistic regression analyses examined predictor variables (age, sex, country of birth, remoteness, education level, employment, relationship status, number of household members, living with parents, receiving the COVID financial supplement from government and index of relative socio-economic advantage and disadvantage) and their distributions over the outcome variable COVID testing.ResultsYouths aged 16-17 were more likely to live at home than youths aged 20-21 years. The strongest predictor of COVID testing was living in major cities (regardless of living with or without parents). Changed household composition was significantly associated with COVID testing among the youths living in the parental home. While among the respondents living without their parents, living with multiple household members and low or no cohesion among household members was associated with higher rates of COVID testing.ConclusionOur study revealed young people have been very good at getting tested for COVID. To further incentivise testing in this age group, we should consider providing this age group with continued financial and social support while awaiting the outcome of the test and during any isolation.Strengths and limitations of this studyLarge national cohort of young people strengthened the findings of the study and allowing us to examine the factors associated with COVID testing for the first time in Australia.A broad-based assessment of potential predictors of COVID testing, including sociodemographic and coronavirus specific factor.Cross-sectional observational design limits causal inference.Self-reported information about COVID testing can be subject to recall as well as social desirability bias.

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Warning “Don’t spread” vs. “Don’t be a spreader” to prevent the COVID-19 pandemic

Cited by 6 publications

References 0 publications

Behavioural barriers to COVID-19 testing in Australia: Two national surveys to identify barriers and estimate prevalence by health literacy level

Behavioural barriers to COVID-19 testing in Australia: Two national surveys to identify barriers and estimate prevalence by health literacy level

Behavioural barriers to COVID-19 testing in Australia

Predictors of COVID testing among Australian youth: Insights from the Longitudinal Study of Australian Children

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