Mapping Community Vulnerability to reduced Vaccine Impact in Uganda and Kenya: A spatial Data-driven Approach - Nalwanga, Robinah; Natukunda, Agnes; Zirimenya, Ludoviko; Chi, Primus; Luzze, Henry; Elliott, Alison M; Kaleebu, Pontiano; Trotter, Caroline L.; Webb, Emily L (NIHR open research, 2025)
Background Despite global efforts to improve on vaccine impact, many African countries have failed to achieve equitable vaccine benefits. Reduced vaccine impact may result from interplay between structural, social, and biological factors, that limit communities from fully benefiting from vaccination programs. However, the combined influence of these factors to reduced vaccine impact and the spatial distribution of vulnerable communities remains poorly understood. We developed a Community Vaccine Impact Vulnerability Index (CVIVI) that integrates data on multiple risk factors associated with reduced vaccine impact, to identify communities at risk, and key drivers of vulnerability. Methods The index was constructed using 17 indicators selected through literature review and categorised into structural, social, and biological domains. Secondary data was obtained from national Demographic and Health surveys from Uganda (2016) and Kenya (2022), covering 123 districts and 47 counties, respectively. Percentile rank methodology was used to construct domain-specific and overall vulnerability indices.. Geo-spatial techniques were used to classify and map districts/counties from least to most vulnerable. Results We observed distinct geographical patterns in vulnerability.. In Kenya, the most vulnerable counties were clustered in the northeast and eastern counties such as Turkana, Mandera, and West Polot. In Uganda, vulnerability was more dispersed, with the most vulnerable districts in the northeast (e.g. Amudat, Lamwo) and southwest e.g. Buliisa,Kyenjojo). Key drivers of vulnerability included long distance to health facilities, low maternal education, poverty, malnutrition, limited access to postnatal care, and limited access to mass media. Some areas with high vaccine coverage also showed high vulnerability, suggesting coverage data may not reliably reflect vaccine impact. Each community showed a unique vulnerability profile, shaped by different combinations of social, structural and biological factors, highlighting the need for context specific interventions. Conclusions The CVIVI is a useful tool for identifying vulnerable communities and underlying factors. It can guide the design of tailored strategies to improve vaccine impact in vulnerable settings.