Spatiotemporal variation in risk of Shigella infection in childhood: a global risk mapping and prediction model using individual participant data

dc.bibliographicCitation.firstPagee373
dc.bibliographicCitation.issue3
dc.bibliographicCitation.journalTitleThe Lancet Global Healtheng
dc.bibliographicCitation.lastPagee384
dc.bibliographicCitation.volume11
dc.contributor.authorBadr, Hamada S.
dc.contributor.authorColston, Josh M.
dc.contributor.authorNguyen, Nhat-Lan H.
dc.contributor.authorChen, Yen Ting
dc.contributor.authorBurnett, Eleanor
dc.contributor.authorAli, Syed Asad
dc.contributor.authorRayamajhi, Ajit
dc.contributor.authorSatter, Syed M.
dc.contributor.authorVan Trang, Nguyen
dc.contributor.authorEibach, Daniel
dc.contributor.authorKrumkamp, Ralf
dc.contributor.authorMay, Jürgen
dc.contributor.authorAdegnika, Ayola Akim
dc.contributor.authorManouana, Gédéon Prince
dc.contributor.authorKremsner, Peter Gottfried
dc.contributor.authorChilengi, Roma
dc.contributor.authorHatyoka, Luiza
dc.contributor.authorDebes, Amanda K.
dc.contributor.authorAteudjieu, Jerome
dc.contributor.authorFaruque, Abu S. G.
dc.contributor.authorHossain, M. Jahangir
dc.contributor.authorKanungo, Suman
dc.contributor.authorKotloff, Karen L
dc.contributor.authorMandomando, Inácio
dc.contributor.authorNisar, M. Imran
dc.contributor.authorOmore, Richard
dc.contributor.authorSow, Samba O.
dc.contributor.authorZaidi, Anita K. M.
dc.contributor.authorLambrecht, Nathalie
dc.contributor.authorAdu, Bright
dc.contributor.authorPage, Nicola
dc.contributor.authorPlatts-Mills, James A.
dc.contributor.authorMavacala Freitas, Cesar
dc.contributor.authorPelkonen, Tuula
dc.contributor.authorAshorn, Per
dc.contributor.authorMaleta, Kenneth
dc.contributor.authorAhmed, Tahmeed
dc.contributor.authorBessong, Pascal
dc.contributor.authorBhutta, Zulfiqar A.
dc.contributor.authorMason, Carl
dc.contributor.authorMduma, Estomih
dc.contributor.authorOlortegui, Maribel P.
dc.contributor.authorPeñataro Yori, Pablo
dc.contributor.authorLima, Aldo A. M.
dc.contributor.authorKang, Gagandeep
dc.contributor.authorHumphrey, Jean
dc.contributor.authorNtozini, Robert
dc.contributor.authorPrendergast, Andrew J.
dc.contributor.authorOkada, Kazuhisa
dc.contributor.authorWongboot, Warawan
dc.contributor.authorLangeland, Nina
dc.contributor.authorMoyo, Sabrina J.
dc.contributor.authorGaensbauer, James
dc.contributor.authorMelgar, Mario
dc.contributor.authorFreeman, Matthew
dc.contributor.authorChard, Anna N.
dc.contributor.authorThongpaseuth, Vonethalom
dc.contributor.authorHoupt, Eric
dc.contributor.authorZaitchik, Benjamin F.
dc.contributor.authorKosek, Margaret N.
dc.date.accessioned2023-06-02T15:03:21Z
dc.date.available2023-06-02T15:03:21Z
dc.date.issued2023
dc.description.abstractBACKGROUND: Diarrhoeal disease is a leading cause of childhood illness and death globally, and Shigella is a major aetiological contributor for which a vaccine might soon be available. The primary objective of this study was to model the spatiotemporal variation in paediatric Shigella infection and map its predicted prevalence across low-income and middle-income countries (LMICs). METHODS: Individual participant data for Shigella positivity in stool samples were sourced from multiple LMIC-based studies of children aged 59 months or younger. Covariates included household-level and participant-level factors ascertained by study investigators and environmental and hydrometeorological variables extracted from various data products at georeferenced child locations. Multivariate models were fitted and prevalence predictions obtained by syndrome and age stratum. FINDINGS: 20 studies from 23 countries (including locations in Central America and South America, sub-Saharan Africa, and south and southeast Asia) contributed 66 563 sample results. Age, symptom status, and study design contributed most to model performance followed by temperature, wind speed, relative humidity, and soil moisture. Probability of Shigella infection exceeded 20% when both precipitation and soil moisture were above average and had a 43% peak in uncomplicated diarrhoea cases at 33°C temperatures, above which it decreased. Compared with unimproved sanitation, improved sanitation decreased the odds of Shigella infection by 19% (odds ratio [OR]=0·81 [95% CI 0·76-0·86]) and open defecation decreased them by 18% (OR=0·82 [0·76-0·88]). INTERPRETATION: The distribution of Shigella is more sensitive to climatological factors, such as temperature, than previously recognised. Conditions in much of sub-Saharan Africa are particularly propitious for Shigella transmission, although hotspots also occur in South America and Central America, the Ganges-Brahmaputra Delta, and the island of New Guinea. These findings can inform prioritisation of populations for future vaccine trials and campaigns. FUNDING: NASA, National Institutes of Health-The National Institute of Allergy and Infectious Diseases, and Bill & Melinda Gates Foundation.eng
dc.description.versionpublishedVersioneng
dc.identifier.urihttps://oa.tib.eu/renate/handle/123456789/12319
dc.identifier.urihttp://dx.doi.org/10.34657/11351
dc.language.isoeng
dc.publisherOxford : Elsevier
dc.relation.doihttps://doi.org/10.1016/s2214-109x(22)00549-6
dc.relation.essn2214-109X
dc.rights.licenseCC BY 4.0 Unported
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subject.ddc610
dc.subject.otherAfrica south of the Saharaeng
dc.subject.otherchildeng
dc.subject.otherdiarrheaeng
dc.subject.otherfamily sizeeng
dc.subject.otherglobal healtheng
dc.titleSpatiotemporal variation in risk of Shigella infection in childhood: a global risk mapping and prediction model using individual participant dataeng
dc.typeArticleeng
dc.typeTexteng
tib.accessRightsopenAccess
wgl.contributorPIK
wgl.subjectMedizin, Gesundheitger
wgl.typeZeitschriftenartikelger
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