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Front of pack food labels (FOPLs) provide accessible nutritional information to guide consumer choice. Using an online experiment with a large representative British sample, we aimed to examine whether FOPLs improve participants’ ability to identify the healthiness of foods and drinks. The primary aim was to compare ability to rank between FOPL groups and a no label control. Adults (≥18 years), recruited from the NatCen panel, were randomised to one of five experimental groups (Multiple Traffic Light, MTL; Nutri-Score, N-S; Warning Label, WL; Positive Choice tick, PC; no label control). Stratification variables were year of recruitment to panel, sex, age, government office region, and household income. Packaging images were created for three versions, varying in healthiness, of six food and drink products (pizza, drinks, cakes, crisps, yoghurts, breakfast cereals). Participants were asked to rank the three product images in order of healthiness. Ranking was completed on a single occasion and comprised a baseline measure (with no FOPL), and a follow-up measure including the FOPL as per each participant’s experimental group. The primary outcome was the ability to accurately rank product healthiness (all products ranked correctly vs. any incorrect). In 2020, 4504 participants had complete data and were included in the analysis. The probability of correct ranking at follow-up, and improving between baseline and follow-up, was significantly greater across all products for the N-S, MTL and WL groups, compared to control. This was seen for only some of the products for the PC group. The largest effects were seen for N-S, followed by MTL. These analyses were adjusted for stratification variables, ethnicity, education, household composition, food shopping responsibility, and current FOPL use. Exploratory analyses showed a tendency for participants with higher compared to lower education to rank products more accurately. Conclusions: All FOPLs were effective at improving participants’ ability to correctly rank products according to healthiness in this large representative British sample, with the largest effects seen for N-S, followed by MTL.

In light of issues such as increasing unit nonresponse in surveys, several studies argue that social media sources such as Twitter can be used as a viable alternative. However, there are also a number of shortcomings with Twitter data such as questions about its representativeness of the wider population and the inability to validate whose data you are collecting. A useful way forward could be to combine survey and Twitter data to supplement and improve both. To do so, consent within a survey is first needed. This study explores the consent decisions in three large representative surveys of the adult British population to link Twitter data to survey responses and the impact that demographics and survey mode have on these outcomes. Findings suggest that consent rates for data linkage are relatively low, and this is in part mediated by mode, where face-to-face surveys have higher consent rates than web versions. These findings are important to understand the potential for linking Twitter and survey data but also to the consent literature generally.

ObjectivesTo examine public views on COVID-19 vaccination and consider the implications for communications and targeted support.DesignCross-sectional study.SettingOnline and telephone nationally representative survey in Great Britain, January to February 2021.Participants4978 adults. Survey response rate was 84%, among the 5931 panellists invited.Main outcome measuresSociodemographic characteristics (age, gender, ethnicity, education, financial status), COVID-19 status, vaccine acceptance, trust in COVID-19 vaccination information sources, perceptions of vaccination priority groups and perceptions of importance of second dose.ResultsCOVID-19 vaccine acceptance (83%) was associated with increasing age, higher level of education and having been invited for vaccination. Acceptance decreased with unconfirmed past COVID-19, greater financial hardship and non-white British ethnicity; black/black British participants had lowest acceptance. Overall, healthcare and scientific sources of information were most trusted. Compared with white British participants, other ethnicities had lower trust in healthcare and scientific sources. Those with lower educational attainment or financial hardship had lower trust in healthcare and scientific sources. Those with no qualifications had higher trust in media and family/friends. While trust was low overall in community or faith leaders, it was higher among those with Asian/Asian British and black/black British ethnicity compared with white British participants. Views of vaccine prioritisation were mostly consistent with UK official policy but there was support for prioritising additional groups. There was high support for having the second vaccine dose.ConclusionsTargeted engagement is needed to address COVID-19 vaccine hesitancy in non-white British ethnic groups, in younger adults, and among those with lower education, greater financial hardship and unconfirmed past infection. Healthcare professionals and scientific advisors should play a central role in communications and tailored messaging is needed for hesitant groups. Careful communication around vaccination prioritisation continues to be required.

Linked survey and Twitter data present an unprecedented opportunity for social scientific analysis, but the ethical implications for such work are complex—requiring a deeper understanding of the nature and composition of Twitter data to fully appreciate the risks of disclosure and harm to participants. In this article, we draw on our experience of three recent linked data studies, briefly discussing the background research on data linkage and the complications around ensuring informed consent. Particular attention is paid to the vast array of data available from Twitter and in what manner it might be disclosive. In light of this, the issues of maintaining security, minimizing risk, archiving, and reuse are applied to linked Twitter and survey data. In conclusion, we reflect on how our ability to collect and work with Twitter data has outpaced our technical understandings of how the data are constituted and observe that understanding one’s data is an essential prerequisite for ensuring best ethical practice.