Geographical distribution of risk factors for invasive non-typhoidal Salmonella at the subnational boundary level in sub-Saharan Africa

• Published in: BMC infectious diseases 2021-06, Vol.21 (1), p.1-529, Article 529
• Main Authors: Lee, Jung-Seok, Mogasale, Vittal, Marks, Florian, Kim, Jerome
• Format: Article
• Language: English
• Subjects: Body mass index; Composite index for iNTS risk factors; Datasets; Disease; Distribution; Drinking water; Geographical distribution; Geographical variation of risk factors; HIV; Human immunodeficiency virus; Infectious diseases; Invasive non-typhoidal Salmonella; Malaria; Malnutrition; Population; Population studies; Risk analysis; Risk factors; Risk factors for iNTS; Risk levels; Salmonella; Salmonellosis; Surveillance; Vector-borne diseases
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-021-06198-1

Invasive non-typhoidal Salmonella (iNTS) is a growing health-concern in many parts of sub-Saharan Africa. iNTS is associated with fatal diseases such as HIV and malaria. Despite high case fatality rates, the disease has not been given much attention. The limited number of population-based surveillance studies hampers accurate estimation of global disease burden. Given the lack of available evidence on the disease, it is critical to identify high risk areas for future surveillance and to improve our understanding of iNTS endemicity. Considering that population-based surveillance data were sparse, a composite index called the iNTS risk factor (iNRF) index was constructed based on risk factors that commonly exist across countries. Four risk factors associated with the prevalence of iNTS were considered: malaria, HIV, malnutrition, and safe water. The iNRF index was first generated based on the four risk factors which were collected within a 50 km radius of existing surveillance sites. Pearson product-moment correlation was used to test statistical associations between the iNRF index and the prevalence of iNTS observed in the surveillance sites. The index was then further estimated at the subnational boundary level across selected countries and used to identify high risk areas for iNTS. While the iNRF index in some countries was generally low (i.e. Rwanda) or high (i.e. Cote d'Ivoire), the risk-level of iNTS was variable not only by country but also within a country. At the provincial-level, the highest risk area was identified in Maniema, the Democratic Republic of Congo, whereas Dakar in Senegal was at the lowest risk. The iNRF index can be a useful tool to understand the geographically varying risk-level of iNTS. Given that conducting a population-based surveillance study requires extensive human and financial resources, identifying high risk areas for iNTS prior to a study implementation can facilitate an appropriate site-selection process in the future.