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The Vaccine Trust Framework: mixed-method development of a tool for understanding and quantifying trust in health systems and vaccines
(Elsevier Ltd, 2025-09) Muhula PhD, S O; Osur PhD, J; et al...
Summary
Trust is a key component of vaccine demand; however, there is no consensus on how to define trust and a lack of actionable, contextually grounded measurement tools validated in low-income and middle-income countries.
Trust is a key component of vaccine demand; however, there is no consensus on how to define trust and a lack of actionable, contextually grounded measurement tools validated in low-income and middle-income countries.
Aim
To develop and validate a Vaccine Trust Framework and a trust measurement tool that can be used to leverage trust to drive resilient vaccine demand.
To develop and validate a Vaccine Trust Framework and a trust measurement tool that can be used to leverage trust to drive resilient vaccine demand.
Methods
An exploratory sequential mixed-methods study was conducted.
An exploratory sequential mixed-methods study was conducted.
- Ethnographic research in Nigeria, Kenya, and Pakistan to define trust in the context of childhood, HPV, and COVID-19 vaccines.
- Validation through a nationally representative survey of caregivers of adolescents in Kenya and Pakistan.
- Psychometric assessment using confirmatory factor analysis and logistic regression.
Findings
-
The Vaccine Trust Framework consists of
four interlinked domains and
15 measurable dimensions:
Health system promise, health system delivery, vaccine promise, and vaccine delivery. - Survey data were collected from 3670 participants in Kenya and 3734 participants in Pakistan.
- Trust influenced vaccine behaviour and intentions, supported by associations between quantitative trust scores and vaccination status.
- Regional variation in trust was observed within Kenya and Pakistan, aligning with qualitative perceptions of local vaccine and health systems.
Interpretation
The Vaccine Trust Framework provides a validated and contextually grounded tool for assessing trust in health systems and vaccines in low-income and middle-income countries.
It can be used as a:
The Vaccine Trust Framework provides a validated and contextually grounded tool for assessing trust in health systems and vaccines in low-income and middle-income countries.
It can be used as a:
- Prognostic tool to anticipate vaccine demand
- Intervention design aid to support trust-building strategies
- Trust measurement tool within intervention or monitoring studies
Digital tracking of girls exposed to community led alternative rites of passage to prevent female genital mutilation/cutting, and child, early and forced marriages in Kenya: a longitudinal study
(Frontiers in Reproductive Health, 2025-05) Kawai, David; Mbogo, Bernard; Opanga, Yvonne; Muhula, Samuel; Esho, Tammary C.; Conradi, Hilke; Rutto, Viola J.; Lugayo, Denge; Matanda, Dennis J
Introduction: Female genital mutilation/cutting (FGM/C) and child marriage (CEFM) are harmful practices that are a human rights violation. For decades, many interventions have been implemented to end these practices. One such intervention is the Alternative Rite of Passage (ARP), which allows girls to go through a meaningful rite of passage without the cut. The ARPs have come under scrutiny due to a lack of data to show how effective ARPs have been. This study aimed to establish the effect of the Community-Led Alternative Rite of Passage (CL-ARP) model on incidences of FGM/C, CEFM and keeping girls and young women in school.
Methods: The study adopted a longitudinal design where girls and young women were enrolled into the CL-ARP programme and later followed up for over three years to assess the effectiveness of the CL-ARP model in preventing incidences of FGM/C, CEFM and keeping girls in school. A total of 2,647 girls aged 10–23 years who resided in Kajiado County were recruited and followed up post-exposure to CL-ARP. Data analysis involved conducting descriptive and logistic regression analyses.
Results: The CL-ARP programme kept 98% of girls free of FGM/C, 99% free of CEFM and 98% kept in school. 41 cases of FGM/C, 12 cases of CEFM and 48 cases of school dropouts were reported three years post-exposure. Girls who underwent FGM/C had been kept free of FGM/C for an average of 39.5 months, those who experienced CEFM had been kept free of CEFM for an average of 40.2 months, and those who dropped out of school had been kept in school for an average of 38.5 months. Girls and young women who experienced instances of threats/violence were more likely to experience FGM/C, CEFM and drop out of school than those who had not.
Conclusions: The CL-ARP programme was successful in keeping the majority of girls and young women free of FGM/C and CEFM, and retained in school post-enrollment. Reported cases of FGM/C, CEFM and school dropouts underline the importance of considering other contextual factors such as gender-based violence that may continue to put girls and young women at risk despite embracing CL-ARP.
Forecasting acute childhood malnutrition in Kenya using machine learning and diverse sets of indicators
(PLoS One, 2025-05) Tadesse, Girmaw Abebe; Ferguson, Laura; Robinson, Caleb; Kuria, Shiphrah
Objectives: Malnutrition is a leading cause of morbidity and mortality for children under-5
globally. Low- and middle-income countries, such as Kenya, bear the greatest burden of
malnutrition. The Kenyan government has been collecting clinical indicators, including on
malnutrition, using District Health Information Software-2 (DHIS2) for over a decade. We
aim to address the existing gap in decision-makers’ ability to develop and utilize malnu trition forecasting capabilities for timely interventions. Specifically, our objectives include:
develop a spatio-temporal machine learning model to forecast acute malnutrition among
children in Kenya using DHIS2 data, enhance forecasting capability by integrating exter nal complementary indicators, such as publicly available satellite imagery-driven signals,
and forecast acute malnutrition at various stages and time horizons, including moderate,
severe, and aggregated cases.
Methods: We propose a framework to forecast malnutrition risk for each sub-county in
Kenya based on clinical indicators and remote sensory data. To achieve this, we first
aggregate clinical indicators and remotely sensed satellite data, specifically gross pri mary productivity measurements, to the sub-county level. We then label the rate of chil dren diagnosed with acute malnutrition at the sub-county level using the standard Inte grated Food Security Phase Classification for Acute Malnutrition. We then apply and
compare several methods for forecasting malnutrition risk in Kenya using data collected
from January 2019 to February 2024. As a baseline, we used a Window Average model,
which captures the current practice at the Kenyan Ministry of Health. We also trained
machine learning models, such as Logistic Regression and Gradient Boosting, to forecast acute malnutrition risk based on observed indicators from prior months. Different
metrics, mainly Area Under Receiver Operating Characteristic Curve (AUC), were used
to evaluate the forecasting performance by comparing their forecast values to known
values on a hold-out test set.
Results: We found that machine learning based models consistently outperform the Win dow Average baselines on forecasting sub-county malnutrition rates in Kenya. For exam ple, the Gradient Boosting model achieves a mean AUC of 0.86 when forecasting with
a 6-month time horizon, compared to an AUC of 0.73 achieved by the Window Average
model. The Window Average method particularly fails to correctly forecast malnutrition in
parts of West and Central Kenya where the acute malnutrition rate is variable over time
and typically less than 15%. We further found that machine learning models with satellite based features alone also outperform Window Averaging baselines, while not needing
clinical data at inference time. Finally, we found that recently observed outcomes and the
remotely sensed data are key indicators. Our results demonstrate the ability of machine
learning models to accurately forecast malnutrition in Kenya at a sub-county level from a
variety of indicators.
Conclusions: To the best of the authors’ knowledge, this work is the first to use clini cal indicators collected via DHIS2 to forecast acute malnutrition in childhood at the sub county level in Kenya. This work represents a foundational step in developing a broader
childhood malnutrition forecasting framework, capable of monitoring malnutrition trends
and identifying impending malnutrition peaks across more than 80 low- and middleincome countries collecting similar DHIS2 datasets.