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Speaking to Hearts Before Minds: Increasing Influenza Vaccine Uptake During COVID-19

In 2019, the UK health secretary Matt Hancock said that he is “open” to making vaccines compulsory, and Labour MP Paul Sweeney argued that failure to vaccinate children should be a “criminal offence”. But mandates are difficult to enforce, and punishments diminish public trust. In addition, people still opt out of mandatory policies, and effectiveness increases when people freely comply.[1] Instead of mandates, we advocate behavioural approaches that preserve individual freedom,[2] and agree with Professor Heidi Larson that additional emphasis should be placed on public perspectives when planning vaccine policies and programmes.[3]

Public health messaging about vaccines is particularly important in light of the COVID-19 pandemic. In April 2020, the United Kingdom’s ‘Vaccine Taskforce’ convened, and, in May 2020, the United States’ ‘Operation Warp Speed’ took off. This speed elicited optimism among some, but handed a megaphone to the anti-vaccination movement. Del Bigtree, founder of the Information Consent Action Network, cautioned that, “You shouldn’t rush to create a product you can inject into perfectly healthy people without doing proper safety studies”. Here, identical factual information – a vaccine is being developed quickly – elicited reasoned responses that were both optimistic and pessimistic. However, intuitions come first and strategic reasoning comes second.[4] Where public health messages do not align with people’s automatic intuitions, factual and reasoned information may fall on deaf ears.

On September 21, we conducted an online experiment to determine if public health messages aligned with people’s political intuitions influenced their intentions to take up the influenza vaccine.[5] Influenza vaccinations have long been important, but are particularly important now in the context of COVID-19 because co-infection increases mortality rates.[6] We recruited 192 participants living in England, aged 50 years+, who had not already vaccinated this season. Half of these participants identified as being affiliated with the Labour party, and half with the Conservative party. Participants viewed a message either aligned or unaligned with their automatic political intuitions (see Figures 1 and 2). Then they stated how much they agreed with a statement about their intentions to take up the influenza vaccine this season on a 7-point scale, where higher numbers indicated more positive intentions.

Fig 1. Left-Wing Message (aligned with Labour)
Fig 2. Right-Wing Message (aligned with Conservative)

Professor Jonathan Haidt describes the automatic intuitions we set out to influence as moral foundations.[4] Typically, people who identify as being more left-wing are most strongly influenced by their care and fairness intuitions (a desire to prevent harm to others and to ensure equality). In contrast, people who identify as being more right-wing are more strongly influenced by the remaining foundations: purity (a desire to avoid contaminants), authority (to preserve traditions), loyalty (to strengthen group bonds), and liberty (to preserve individual freedom).

Research conducted in the United States and Australia has already identified some of the foundations associated with parental vaccine hesitancy, and suggests that public health messages can be framed to increase parents’ intentions.[7,8] For example, a message designed to promote purity might say: Boost your child’s natural defenses against diseases! – Vaccinate! These proposals are a good start, but without evidence that they are likely to be effective, public health practitioners have little reason to prefer them to the messages developed in-house. The messages used in the present study were informed by messages used in a previous study that significantly altered people’s intentions to recycle.[9]

Our main prediction was that our left-wing message would increase labour participants’ intentions, and our right-wing message would increase conservative participants’ intentions. We did not find this. As shown in Figure 3, there was no substantial effect of the messages. One explanation is that the moral foundations used in our advertisements were not relevant in a UK context, which we plan to address in future work. We aim to conduct a general UK survey describing moral foundations in the population and use the survey results to inform a collaborative online workshop with public contributors and health specialists, which is in keeping with Professor Heidi Larson’s calls to involve public perspectives. This pilot study lays the groundwork for such future research.

Fig 3. Results of the study testing the effects of messages on vaccination intentions as measured by average agreement with the statement: “I intend to receive an influenza vaccination this season [2020/21].”

We asked people some follow up questions too. In a free-text box, participants were asked to explain their intentions to (or not to) vaccinate. Their explanations largely fell within five categories, which, in addition to their foundations, may have been influenced by the messages they read: Protect Self, Protect Others, Protect the NHS, Being Eligible/Invited, and Habits. We also asked questions about people’s intentions of taking up a COVID-19 vaccination and wearing a face mask. Similar to recent research,[10] people were more likely to express intentions to take up a future COVID-19 vaccination (72%) than the current influenza vaccination (65%). We suspect that these expressed intentions may be a bit optimistic. Indeed, most participants (89%) also expressed that they would wear a face mask in a store that did not require them to do so, which is higher than our casual observations at the grocery store around the time of the experiment (before additional penalties were introduced). Acquiescence bias may have led our participants to be agreeable in this survey, particularly as participants just saw messages promoting health-related behaviour. But this need not preclude identifying meaningful differences between randomised conditions. Our research team looks forward to better understanding the intuitive influences on vaccination behaviour.

Kelly Ann Schmidtke (Assistant Professor) and Laura Kudrna (Research Fellow)

References:

  1. Salmon DA, et al. Compulsory vaccination and conscientious or philosophical exemptions: past, present, and future. Lancet. 2006;367(9508):436-42.
  2. Sunstein C & Thaler R. Libertarian Paternalism. Am Econ Rev. 2003; 93(2): 175-9.
  3. Larson HJ et al. Addressing the vaccine confidence gap. Lancet. 2011;378:526-35.
  4. Haidt J. The righteous mind: why good people are divided by politics and religion. New York: Pantheon Books; 2012.
  5. U.S. National Library of Medicine. ClinicalTrials.gov Influenza 2020/2021. NCT04546854. 14 September 2020.
  6. Iacobucci G. Covid-19: Risk of death more than doubled in people who also had flu, English data show. BMJ. 2020;370:m3720.
  7. Amin AB, et al. Association of moral values with vaccine hesitancy. Nat Hum Behav. 2017;1(12):873-80.
  8. Rossen I, et al. Accepters, fence sitters, or rejecters: moral profiles of vaccination attitudes. Soc Sci Med. 2019;224(1):23-7.
  9. Kidwell B, et al. Getting Liberals and Conservatives to Go Green: Political Ideology and Congruent Appeals. J Cons Res. 2013; 40(2):350–67.
  10. Boseley S. Coronavirus: fifth of people likely to refuse Covid vaccine, UK survey finds. The Guardian. 24 September 2020.

The Land War in the Fight Against COVID-19

Gone are the days of thinking there is a quick fix to the COVID-19 pandemic. Another country-wide lockdown would reduce COVID-19 infection, but at the same time would damage the economy and pose a threat to other long-term health conditions, with disproportionate effects on the more disadvantaged groups in society. The Great Barrington Declaration – aiming for herd immunity while sequestering high-risk people – does not bear close examination.[1] Vaccination is not an automatic get out of jail card – we do not yet know when vaccination will be available at the required volume, nor what degree of protection it will confer. So, this is the land war. We must work on supply chains, procedures, detection and contact tracing, getting ever slicker at the operation. Personal protection, social distancing and graded lockdowns can all play a part, but only if they are accepted by the general public, who deserve clear explanations of when, where and why unwelcome restrictions will be imposed and what these restrictions are intended to achieve.

While central government has an obvious role to play, it has become clear that the battle must go local; and the more local the better. The risk of being hospitalised with COVID-19 in Birmingham varies dramatically across the various electoral wards, with the seven-day rolling rate of new cases (for week ending 14 October 2020) ranging from 43.8 per 100,000 in Nechells, to 825.8 in Selly Oak.[2] So, supported by the MRC, NIHR ARC West Midlands and our host hospital (University Hospitals Birmingham NHS Foundation Trust) we are developing a computer application to track the evolving pattern of the COVID-19 pandemic. We have developed software that uses geostatistical models to identify “hot spots”, however one defines them, across a broad space such as an urban conurbation or a country. Within such a space we identify localities at whatever scale is relevant for local decision-making and that the data can support. We can map rates of infection per unit of population in real time on these maps to show the current state of the epidemic and its direction of travel (see Example). These maps can direct decision-makers to specific localities where incidence is increasing rapidly and hence where urgent action is needed.

But there is a problem with policy action directed at small areas and particular communities – dictatorial edicts are likely to provoke resentment rather than effective action, especially when carried out at a very local level. It is one thing to place restrictions across a whole country or even a large city, but quite another to try to lockdown an area such as Lady Pool in Birmingham or Chapel Town in Leeds. Indeed, the disease has highest incidence in BAME communities who may feel victimised or disenfranchised. Already only 18% of people fully comply with UK regulations regarding self-isolation.[3] So here we come to the second use of our application and the maps it produces.

We think that policy-makers should increasingly turn to local communities and ask them to be the architects, not recipients, of policy. In essence we are arguing for an ‘assets-based’ or ‘participatory’ approach based on ‘co-invention’. And here our application can help by providing scientific data at a local level in a form that can be easily assimilated. We are arguing at a local level for the type of thing that Prof Chris Witty used at a national level in his Downing Street presentation with the Prime Minister and Chancellor (12 October 2020). There is evidence that populations relate well to local maps and they are sometimes used in qualitative research as a method to promote discussion among people.[4] The approach we are advocating here, of high-risk spatio-temporal identification, followed by case-area targeted intervention, has proven effective in limiting the spread of cholera outbreaks,[5] and we advocate a similar approach with respect to the COVID-19 pandemic.

We would be pleased to hear from news blog readers regarding:

  1. Your opinions and advice.
  2. Whether you would like to hear more or use the application when it is developed.
  3. Whether you have examples of similar initiatives elsewhere in the world.
  4. Whether you would like to collaborate.

You can contact us at ARCWM@warwick.ac.uk.

Richard Lilford, ARC WM Director; Sam Watson, Senior Lecturer; Peter Diggle, Distinguished Professor at Lancaster University

Example of Real-Time Surveillance of COVID-19

For this example we have aggregated the results to MSOA (middle-layer Super Output Area) level across the catchment area of University Hospitals Birmingham NHS Foundation Trust, although we have retained other areas of Birmingham to make the boundary of the city clear. One could aggregate to smaller or larger levels as needed. A case here is an admission to hospital for COVID-19.

We have produced these outputs as if we were working on March 26 2020 using data from the preceding two weeks. The first thing someone interested in tracking COVID-19 in the city might ask is what is the incidence of the disease that day?

There is a lot of variation across the different MSOAs, with one area standing out as being high (yellow area). The variation here could be explained by differences in demographics or socioeconomic status, and we might want to ask whether any differences are for unexpected reasons. We can break down the incidence into
different components:

Where:

  • Expected is the number of cases we would expect that day from each area based on the size of its population.
  • Observed shows the relative risk in each area associated with observable characteristics
    (age, ethnicity, and deprivation). For example, consider if the average incidence across the city were one case per 10,000 person-days. An area with a larger proportion of older residents would have a high risk; if this risk were double the average then it would have a relative risk of two.
  • Latent is the relative risks in each area due to unexplained factors or unobserved
    variables. Our area with more older people may have an expected incidence of two cases per 10,000 person-days (a ‘baseline’ of 1 per 10,000 person-days times a relative risk of two), but if we observe an average rate of four cases per 10,000 person-days, then there is an additional unexplained relative risk of 2.
  • Posterior SD indicates the predictive variance.

So based on these plots the area with high incidence in the North of Birmingham would appear to be higher than we would expect based on the observed variables by factor of 2 or 3. This may indicate the need for public health intervention. We might finally ask, how this compares to previous days?

The next plot shows the incidence rate ratio, which here is the ratio of incidence compared to seven days prior for each area. A value of one indicates no change, two a doubling, and so forth. One can clearly see that it is above one, i.e. it is increasing, city-wide. The greatest relative increases are centred on the area we identified as being of high concern.



References:

  1. Alwan NA, et al. Scientific consensus on the COVID-19 pandemic: we need to act now. Lancet. 2020.
  2. Public Health England. Coronavirus (COVID-19) in the UK: Interactive Map. 19 October 2020.
  3. Smith LE, et al. Adherence to the test, trace and isolate system: results from a time series of 21 nationally representative surveys in the UK (the COVID-19 Rapid Survey of Adherence to Interventions and Responses [CORSAIR] study). MedRXiv. 2020. [Pre-print].
  4. Boschmann EE, Cubbon E. Sketch maps and qualitative GIS: Using cartographies of individual spatial narratives in geographic research. Professional Geographer. 2014;66(2):236-48.
  5. Ratnayake R, et al. Highly targeted spatiotemporal interventions against cholera epidemics, 2000-19: a scoping review. Lancet Infect Dis. 2020.