ArticleBMJ open2023
Exploring COVID-19 vaccine hesitancy and uptake in Nairobi's urban informal settlements: an unsupervised machine learning analysis of a longitudinal prospective cohort study from 2021 to 2022.
Article in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vaccine hesitancy in context of COVID-19 in East Africa: systematic review and meta-analysis.BMC public health · 2024Pooled it
- Scoping review of COVID-19 vaccines access, equity, hesitancy, and uptake in Kenya: lessons for the next pandemic.Frontiers in health services · 2026Review
- COVID-19 Vaccine Uptake: Prevalence, Health Facility Enablers and Barriers among Adult Tuberculosis Patients across Nairobi County Clinics, Kenya.Journal of tuberculosis research · 2025Article
- Article
- Kenyans' Perceptions of the Risks of COVID-19 Vaccines: A Scoping Review.Pan-African journal of health and environmental science · 2024Article
- Gendered socio-economic and mental health effects of the COVID-19 pandemic among adults living in selected informal settlements in Kenya: an intersectional analysis.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo 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.
designA 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.
settingFive informal settlements in Nairobi, Kenya.
participantsIndividuals 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.
resultsDespite 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<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<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.
conclusionsFindings 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.
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