The Surveillance for Enteric Fever in Asia Project (SEAP), Severe Typhoid Fever Surveillance in Africa (SETA), Surveillance of Enteric Fever in India (SEFI), and Strategic Typhoid Alliance Across Africa and Asia (STRATAA) Population-based Enteric Fever Studies: A Review of Methodological Similarities and Differences

Abstract

Building on previous multicountry surveillance studies of typhoid and others salmonelloses such as the Diseases of the Most Impoverished program and the Typhoid Surveillance in Africa Project, several ongoing blood culture surveillance studies are generating important data about incidence, severity, transmission, and clinical features of invasive Salmonella infections in sub-Saharan Africa and South Asia. These studies are also characterizing drug resistance patterns in their respective study sites. Each study answers a different set of research questions and employs slightly different methodologies, and the geographies under surveillance differ in size, population density, physician practices, access to healthcare facilities, and access to microbiologically safe water and improved sanitation. These differences in part reflect the heterogeneity of the epidemiology of invasive salmonellosis globally, and thus enable generation of data that are useful to policymakers in decision-making for the introduction of typhoid conjugate vaccines (TCVs). Moreover, each study is evaluating the large-scale deployment of TCVs, and may ultimately be used to assess post-introduction vaccine impact. The data generated by these studies will also be used to refine global disease burden estimates. It is important to ensure that lessons learned from these studies not only inform vaccination policy, but also are incorporated into sustainable, low-cost, integrated vaccine-preventable disease surveillance systems.

A Bayesian approach for estimating typhoid fever incidence from large‐scale facility‐based passive surveillance data

“…In many cases, studies simply double the reported cases to account for blood culture sensitivity. 29 Numerous studies make use of a simple multiplier method, 30 , 31 , 32 , 33 which often do not accurately reflect the uncertainty associated with the reporting process. Previous studies have not attempted to integrate data sources to account for potential differences between the observed healthcare seeking and testing probabilities for fever vs the corresponding unobserved probabilities specific to typhoid fever, which our analysis suggests can impact the final adjustment factors.…”

A Bayesian approach for estimating typhoid fever incidence from large-scale facility-based passive surveillance data

“…Numerous studies make use of a simple multiplier method [30][31][32][33] , which often do not accurately reflect the uncertainty associated with the reporting process. Previous studies have not attempted to integrate data sources to account for potential differences between the observed healthcare seeking and testing probabilities for fever versus the corresponding unobserved probabilities specific to typhoid fever, which our analysis suggests can impact the final adjustment factors.…”

Environmental surveillance for Salmonella Typhi as a tool to estimate the incidence of typhoid fever in low-income populations

“…Study boundaries are defined as those areas covered by hospital-based blood culture surveillance and planned healthcare utilisation surveys. In Vellore, this corresponds to the study area originally identified by the Surveillance of Enteric Fever in India (SEFI) program [28] and represents an area of 16 km 2 . In Blantyre, this includes the entire municipality, consisting of the central urban districts and surrounding areas, representing 214 km 2 .…”