Vaccination, herd behavior, and herd immunity

Are the 7C psychological antecedents associated with COVID-19 vaccine behaviours beyond intentions? A cross-sectional study on at-least-one-dose and up-to-date vaccination status, and uptake speed among adults in France

BACKGROUND

Widely documented psychological antecedents of vaccination are confidence in vaccines, complacency, convenience, calculation, collective responsibility (5C model) with the recent addition of confidence in the wider system and social conformism. While the capacity of these seven antecedents (7C) to explain variance in COVID-19 vaccine intentions has been previously documented, we study whether these factors also are associated with vaccine behaviours, beyond intentions.

METHODS

From February to June 2022, we recruited a sample of adults in France, including persons with notified recent SARS-CoV-2 infection, along with relatives and randomly selected non-infected persons. Participants completed self-administered questionnaires assessing COVID-19 vaccination history and the 7C antecedents. We defined vaccination behaviours as three outcomes: at-least-one-dose vaccine status by 2022 (N = 49,019), up-to-date vaccination status (N = 46,566), and uptake speed of first dose (N = 25,998). We conducted multivariable logistic regressions and Cox models.

RESULTS

Among the 49,019 participants, 95.0% reported receipt of at least one dose and 89.8% were up to date with recommendations. All 7C antecedents were significantly associated with the outcomes, although effects were weaker for up-to-date vaccination status and uptake speed. The strongest effects (most vs. least vaccine-favourable attitude level, at-least-one-dose vaccination status) were observed for collective responsibility (OR: 14.44; 95%CI: 10.72-19.45), calculation (OR: 10.29; 95%CI: 7.53-14.05), and confidence in the wider system (OR: 8.94; 95%CI: 6.51-12.27).

CONCLUSION

This study demonstrates that the 7C not only explain vaccine intention, but also vaccine behaviours, and underpins the importance of developing vaccine promotion strategies considering the 7C antecedents.

Herding behavior in COVID-19 vaccine hesitancy in rural Zimbabwe: The moderating role of health information under heterogeneous household risk perceptions

COVID-19 vaccine hesitancy poses a global health threat by potentially delaying the attainment of herd immunity to attenuate infection and transmission. Most governments across the world are engrossed with formulating strategies to surmount conservative group behavior such as vaccine hesitancy typical under risky and uncertain situations such as in the case of COVID-19. This paper examines herding behavior in vaccine hesitancy with a special focus on the moderating role of household access to health information from village health workers under different risk perceptions. We use the 2021 Zimbabwe Vulnerability Assessment Committee cross-section household national survey consisting of 13, 583 valid observations. Our major findings indicate that herding behavior plays a role in rural households' hesitancy to COVID-19 vaccine inoculation. Furthermore, whilst access to health information from village health workers reduces herding behavior in vaccine hesitancy, it does so more when the household perceives itself to be at high risk of contracting COVID-19. Analysing herding behavior in vaccine hesitancy can help policymakers develop more targeted vaccination strategies, such as promoting access to health information through channels like village health workers, especially for households at high risk of contracting COVID-19.

Non-pharmaceutical interventions during the COVID-19 pandemic: A review

Infectious diseases and human behavior are intertwined. On one side, our movements and interactions are the engines of transmission. On the other, the unfolding of viruses might induce changes to our daily activities. While intuitive, our understanding of such feedback loop is still limited. Before COVID-19 the literature on the subject was mainly theoretical and largely missed validation. The main issue was the lack of empirical data capturing behavioral change induced by diseases. Things have dramatically changed in 2020. Non-pharmaceutical interventions (NPIs) have been the key weapon against the SARS-CoV-2 virus and affected virtually any societal process. Travel bans, events cancellation, social distancing, curfews, and lockdowns have become unfortunately very familiar. The scale of the emergency, the ease of survey as well as crowdsourcing deployment guaranteed by the latest technology, several Data for Good programs developed by tech giants, major mobile phone providers, and other companies have allowed unprecedented access to data describing behavioral changes induced by the pandemic. Here, I review some of the vast literature written on the subject of NPIs during the COVID-19 pandemic. In doing so, I analyze 348 articles written by more than 2518 authors in the first 12 months of the emergency. While the large majority of the sample was obtained by querying PubMed, it includes also a hand-curated list. Considering the focus, and methodology I have classified the sample into seven main categories: epidemic models, surveys, comments/perspectives, papers aiming to quantify the effects of NPIs, reviews, articles using data proxies to measure NPIs, and publicly available datasets describing NPIs. I summarize the methodology, data used, findings of the articles in each category and provide an outlook highlighting future challenges as well as opportunities.

Towards a data-driven characterization of behavioral changes induced by the seasonal flu

In this work, we aim to determine the main factors driving self-initiated behavioral changes during the seasonal flu. To this end, we designed and deployed a questionnaire via Influweb, a Web platform for participatory surveillance in Italy, during the 2017 - 18 and 2018 - 19 seasons. We collected 599 surveys completed by 434 users. The data provide socio-demographic information, level of concerns about the flu, past experience with illnesses, and the type of behavioral changes voluntarily implemented by each participant. We describe each response with a set of features and divide them in three target categories. These describe those that report i) no (26%), ii) only moderately (36%), iii) significant (38%) changes in behaviors. In these settings, we adopt machine learning algorithms to investigate the extent to which target variables can be predicted by looking only at the set of features. Notably, 66% of the samples in the category describing more significant changes in behaviors are correctly classified through Gradient Boosted Trees. Furthermore, we investigate the importance of each feature in the classification task and uncover complex relationships between individuals' characteristics and their attitude towards behavioral change. We find that intensity, recency of past illnesses, perceived susceptibility to and perceived severity of an infection are the most significant features in the classification task and are associated to significant changes in behaviors. Overall, the research contributes to the small set of empirical studies devoted to the data-driven characterization of behavioral changes induced by infectious diseases.

Behavioural change models for infectious disease transmission: a systematic review (2010-2015)

We review behavioural change models (BCMs) for infectious disease transmission in humans. Following the Cochrane collaboration guidelines and the PRISMA statement, our systematic search and selection yielded 178 papers covering the period 2010-2015. We observe an increasing trend in published BCMs, frequently coupled to (re)emergence events, and propose a categorization by distinguishing how information translates into preventive actions. Behaviour is usually captured by introducing information as a dynamic parameter (76/178) or by introducing an economic objective function, either with (26/178) or without (37/178) imitation. Approaches using information thresholds (29/178) and exogenous behaviour formation (16/178) are also popular. We further classify according to disease, prevention measure, transmission model (with 81/178 population, 6/178 metapopulation and 91/178 individual-level models) and the way prevention impacts transmission. We highlight the minority (15%) of studies that use any real-life data for parametrization or validation and note that BCMs increasingly use social media data and generally incorporate multiple sources of information (16/178), multiple types of information (17/178) or both (9/178). We conclude that individual-level models are increasingly used and useful to model behaviour changes. Despite recent advancements, we remain concerned that most models are purely theoretical and lack representative data and a validation process.

Using Behavioral Insights to Increase Vaccination Policy Effectiveness

Even though there are policies in place, and safe and effective vaccines available, almost every country struggles with vaccine hesitancy, that is, a delay in acceptance or refusal of vaccination. Consequently, it is important to understand the determinants of individual vaccination decisions to establish effective strategies to support the success of country-specific public health policies. Vaccine refusal can result from complacency, inconvenience, a lack of confidence, and a rational calculation of pros and cons. Interventions should, therefore, be carefully targeted to focus on the reason for non-vaccination. We suggest that there are several interventions that may be effective for complacent, convenient, and calculating individuals whereas interventions that might be effective for those who lack confidence are scarce. Thus, efforts should be concentrated on motivating the complacent, removing barriers for those for whom vaccination is inconvenient, and adding incentives and additional utility for the calculating. These strategies might be more promising, economic, and effective than convincing those who lack confidence in vaccination.

Quantifying the decisional satisfaction to accept or reject the Human Papillomavirus (HPV) vaccine: a preference for cervical cancer prevention

Objective

Only a portion of the US population is willing to consider HPV vaccination to date. The primary aim of this study is to determine the decisional satisfaction associated with HPV vaccination.

Study Design

This is a prospective survey conducted at an urban college where women 18–26 years old completed a decisional satisfaction survey about their HPV vaccine experience.

Results

Regardless of the decision to accept or reject HPV vaccination, the decisional satisfaction was very high (mean 5-item score = 21.2 (SD 3.8)). Women without HPV vaccination were decisionally neutral significantly more often than those already vaccinated; 22% were decisionally neutral for the option to accept HPV vaccination at that visit. Cervical cancer prevention was preferred significantly more often than genital wart prevention in all analyses.

Conclusions

Targeting those who are decisionally neutral about HPV vaccination may result in a higher uptake of HPV vaccination.