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. 2024 Aug 21:8:27.
doi: 10.12688/wellcomeopenres.18054.2. eCollection 2023.

Understanding the epidemiology of iNTS disease in Africa in preparation for future iNTS- vaccine studies in endemic countries: Seroepidemiology in Africa of iNTS (SAiNTS) Study Protocol: Malawi site [Version 9.0]

Helen Dale  1   2 , Esmeda Chirwa  1   2 , Priyanka Patel  2 , Georgina Makuta  2 , Felistas Mwakiseghile  2 , Theresa Misiri  2 , Innocent Kadwala  2 , Maurice Mbewe  2 , Happy Banda  2 , Niza Silungwe  2 , Kenneth Chizani  2 , Paul Kambiya  2 , Marc Henrion  2 , Neil French  1 , Tonny Nyirenda  3 , Melita Gordon  1   2 ; Vacc-iNTS
Affiliations

Understanding the epidemiology of iNTS disease in Africa in preparation for future iNTS- vaccine studies in endemic countries: Seroepidemiology in Africa of iNTS (SAiNTS) Study Protocol: Malawi site [Version 9.0]

Helen Dale et al. Wellcome Open Res. .

Abstract

Background: Non-typhoidal Salmonella (NTS) are a major cause of bloodstream infections amongst children in sub-Saharan Africa. A clear understanding of the seroepidemiology and correlates of protection for invasive NTS (iNTS) in relation to key risk factors (malaria, anaemia, malnutrition) in children in Africa is needed to inform strategies for disease control including vaccine implementation.

Methodology: The SAiNTS study is a prospective community cohort study with paired serology samples from 2500 Malawian children 0-5 years at baseline and three months to measure age-stratified acquisition of lipopolysaccharide (LPS) O-antigen antibody (IgG) and serum bactericidal activity to the main serovars causing iNTS ( Salmonella Typhimurium and S. Enteritidis). Children are selected from mapped and censused randomly selected households in Chikwawa, Malawi; an area with substantial malaria burden. The sampling framework is set within a malaria vaccination (RTS,S/ AS01) phase 4 cluster randomized trial, known as the Epidemiology Study of Malaria Transmission Intensity (EPI-MAL), allowing exploration of the impact of malaria vaccination on acquisition of immunity to NTS. Risk factor data for invasive disease will be collected using rapid diagnostic tests for malaria and anaemia, anthropometry for malnutrition, and a validated questionnaire for indicators of socioeconomic status, water and sanitation. All data will be recorded through electronic case report forms using the REDCap and the Open Data Kit (ODK) platforms. Stool sample analysis includes NTS culture and pan-Salmonella polymerase chain reaction to assess enteric exposure and biomarkers of environmental enteric dysfunction. Cases with iNTS disease will be followed up for comparison with community controls.

Conclusions: The final cohort of 2500 children will allow investigation into the impact of risk factors for iNTS on the acquisition of immunity in children 0-5 years in an endemic setting, including comparisons to partner seroepidemiology studies in three other sub-Saharan African sites (1000 children per site). The data generated will be key to informing iNTS disease control measures including targeted risk factor interventions and vaccine implementation through investigation of correlates of protection and identifying windows of immune susceptibility in at-risk populations.

Keywords: iNTS; immunoepidemiology; susceptibility; malaria; children; non-typhoidal salmonella.

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Conflict of interest statement

No competing interests were disclosed.

Figures

Figure 1.
Figure 1.. Overall Vacc-iNTS strategy.
Figure 2.
Figure 2.
A: Age distribution of iNTS cases (0–60 months). B: Age distribution of anti S. Typhimurium LPS antibodies .
Figure 3.
Figure 3.. Theoretical framework for a model of exposures and protection in iNTS disease.
Figure 4.
Figure 4.. Study recruitment summary flowchart (see supplementary documents for detailed flowcharts).
Figure 5.
Figure 5.. Margin of error and 95% Confidence Intervals across the range of seroprevalences.
Figure 6.
Figure 6.
A: Power for detecting seroconversion following enteric infection. B: Power for detecting change in SBA following enteric salmonella exposure (8%) prevalence (N=2500).
Figure 7.
Figure 7.
A: Power for detecting change in SBA due to malaria infection. B: Example of data from a single simulation (malaria). C: Power curves for detecting impact of anaemia on mean NTS antibody level.
Figure 8.
Figure 8.
A: Power for detecting change in SBA due to malaria infection. B: Example data from a single simulation (malaria). C: Power for detecting change in SBA due to Anaemia. D: Power for detecting change in SBA due to malaria vaccination (N=2500).
See this image and copyright information in PMC

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