Exploring COVID-19 vaccine hesitancy and uptake in Nairobi\u27s urban informal settlements: An unsupervised machine learning analysis of a longitudinal prospective cohort study from 2021 to 2022

Objectives: To illustrate the utility of unsupervised machine learning compared with traditional methods of analysis by identifying archetypes within the population that may be more or less likely to get the COVID-19 vaccine. Design: A longitudinal prospective cohort study (n=2009 households) with recurring phone surveys from 2020 to 2022 to assess COVID-19 knowledge, attitudes and practices. Vaccine questions were added in 2021 (n=1117) and 2022 (n=1121) rounds. Setting: Five informal settlements in Nairobi, Kenya. Participants: Individuals from 2009 households included. Outcome Measures and Analysis: Respondents were asked about COVID-19 vaccine acceptance (February 2021) and vaccine uptake (March 2022). Three distinct clusters were estimated using K-Means clustering and analysed against vaccine acceptance and vaccine uptake outcomes using regression forest analysis. Results: Despite higher educational attainment and fewer concerns regarding the pandemic, young adults (cluster 3) were less likely to intend to get the vaccine compared with cluster 1 (41.5% vs 55.3%, respectively; p \u3c 0.01). Despite believing certain COVID-19 myths, older adults with larger households and more fears regarding economic impacts of the pandemic (cluster 1) were more likely to ultimately to get vaccinated than cluster 3 (78% vs 66.4%; p \u3c 0.01), potentially due to employment requirements. Middle-aged women who are married or divorced and reported higher risk of gender-based violence in the home (cluster 2) were more likely than young adults (cluster 3) to report wanting to get the vaccine (50.5% vs 41.5%; p = 0.014) but not more likely to have gotten it (69.3% vs 66.4%; p = 0.41), indicating potential gaps in access and broader need for social support for this group. Conclusions: Findings suggest this methodology can be a useful tool to characterise populations, with utility for improving targeted policy, programmes and behavioural messaging to promote uptake of healthy behaviours and ensure equitable distribution of prevention measures