Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Sep 23:5:24.
doi: 10.3310/nihropenres.13898.3. eCollection 2025.

Mapping Community Vulnerability to reduced Vaccine Impact in Uganda and Kenya: A spatial Data-driven Approach

Robinah Nalwanga  1   2 , Agnes Natukunda  1   2 , Ludoviko Zirimenya  3 , Primus Chi  4 , Henry Luzze  5 , Alison M Elliott  3 , Pontiano Kaleebu  2 , Caroline L Trotter  6 , Emily L Webb  1
Affiliations

Mapping Community Vulnerability to reduced Vaccine Impact in Uganda and Kenya: A spatial Data-driven Approach

Robinah Nalwanga et al. NIHR Open Res. .

Abstract

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.

Keywords: Kenya; Uganda; Vaccine coverage; Vaccine impact; Vulnerability index; Vulnerable communities.

Plain language summary

Vaccination saves millions of lives every year; however, in many African countries, people are still dying from vaccine-preventable diseases. This is often due to low vaccine coverage and differences in how well individuals respond to vaccines. Several factors may contribute to these challenges, including poor access to healthcare services, high levels of poverty, and malnutrition, which collectively are likely to reduce benefits from vaccination programs. Identifying which communities at a risk of reduced vaccine impact and the main driver for their vulnerability requires data-driven approaches that integrate data on multiple risk factors into a single value. In this study, we developed the Community Vaccine Impact Vulnerability Index (CVIVI), which helps to identify geographical areas where communities are less likely to benefit fully from vaccines and vaccination programs. Using the CVIVI, we found out that factors such as high levels of poverty, low maternal education, limited access to mass media, and malnutrition often intersect in specific districts and counties, making these areas susceptible to reduced vaccine impact. For instance, counties such as Turkana in the northwest and Tana River in southeastern Kenya, along with Buliisa district in western Uganda and Amudat district in northern Uganda, were identified as most vulnerable. The index thus enables policymakers and researchers to identify communities at a risk of reduced vaccine impact and highlight barriers contributing to this vulnerability. With this information, the index would serve as a starting point for policymakers, implementers, researchers, and other stakeholders to understand vulnerability to vaccine impact, and design better tailored interventions, ensuring that every community fully benefits from vaccination programs.

PubMed Disclaimer

Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Workflow for assessing community vaccine impact vulnerability.
Figure 2.
Figure 2.. Pairwise correlations between vulnerability indicators in Kenya.
Blue outline represents biological factors, red outline represents structural factors, and green outline represents social factors. Asterisks indicate level of significance: ***p value < 0.001, **p value < 0.01, *p value < 0.05.
Figure 3.
Figure 3.. Pairwise correlation between indicators in Uganda.
Blue outline represents biological factors, red outline represents structural factors, and green outline represents social factors. Asterisks indicate level of significance: ***p value < 0.001, **p value < 0.01, *p value < 0.05. The acronyms used in the Figure 3 and Figure 4 are as follows: VC (Vaccine Coverage), MAL (Malaria prevalence), STU (Stunting prevalence), ANE (Anemia prevalence), RPOP (Rural population), INS (Insurance coverage), HDEL (Home delivery), IMM (No immunization card), PNC (No postnatal care for newborns), DHC (Long distance to nearby health facility), EDU (Low mother’s education level), LWQ (Low wealth quintile), MED (Limited access to mass media), WATS (Access to unimproved water sources), HOUS (Poor housing structures), SAN (Access to unimproved sanitation facilities), and TRAN (Ownership of non-motorized transport means).
Figure 4.
Figure 4.. Spatial Distribution of CVIVI scores across counties in Kenya.
Dark colors represent high vulnerability, and light colors correspond to low score vulnerability. Scores are categorized by groups: 1= least vulnerability, 2 = less vulnerable, 3 = moderately vulnerable 4= More vulnerable, 5= most vulnerable.
Figure 5.
Figure 5.. Percentage distribution of indicators across vulnerability groups in Kenya.
The figure presents the average estimate of key indicators across vulnerability groups, from the most vulnerable (dark shades) to the least vulnerable (light shades). Each panel represents a specific indicator, illustrating its contribution to community vulnerability.
Figure 6.
Figure 6.. Estimates of the vulnerability index across districts in Uganda.
The index scores are grouped onto 5 groups with the dark colors representing high vulnerability scores, and light colors correspond to low score vulnerability scores.
Figure 7.
Figure 7.. Percentage distribution of indicators across vulnerability groups in Uganda.
The figure presents the average estimate of key indicators across vulnerability groups, from the most vulnerable (dark shades) to the least vulnerable (light shades). Each panel represents a specific indicator, illustrating its contribution to community vulnerability.
Figure 8.
Figure 8.. Overall and domain specific vulnerability scores for Wajir and Kilifi counties in Kenya.
Figure 9.
Figure 9.. Spatial distribution of vaccine coverage across counties in Kenya.
Figure 10.
Figure 10.. Spatial distribution of vaccine coverage across districts in Uganda.
Figure 11.
Figure 11.. Correlation plots showing the relationship between vulnerability index scores and Vaccine coverage.
Solid line shows linear regression fit while the shaded part shows the 95% confidence interval.
See this image and copyright information in PMC

References

    1. Immunization.WHO | Regional Office for Africa,2024; [cited 2024 Nov 28]. Reference Source
    1. Rodrigues CMC, Plotkin SA: Impact of vaccines; health, economic and social perspectives. Front Microbiol. 2020;11:1526. 10.3389/fmicb.2020.01526 - DOI - PMC - PubMed
    1. Li X, Mukandavire C, Cucunubá ZM, et al. : Estimating the health impact of vaccination against ten pathogens in 98 low-income and middle-income countries from 2000 to 2030: a modelling study. Lancet. 2021;397(10272):398–408. 10.1016/S0140-6736(20)32657-X - DOI - PMC - PubMed
    1. Mutua MK, Mohamed SF, Porth JM, et al. : Inequities in on-time childhood vaccination: evidence from Sub-Saharan Africa. Am J Prev Med. 2021;60(1 Suppl 1):S11–23. 10.1016/j.amepre.2020.10.002 - DOI - PubMed
    1. Joseph NK, Macharia PM, Ouma PO, et al. : Spatial access inequities and childhood immunisation uptake in Kenya. BMC Public Health. 2020;20(1): 1407. 10.1186/s12889-020-09486-8 - DOI - PMC - PubMed

LinkOut - more resources