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

Badr, H. S., Colston, J. M., Nguyen, N.-L.-H., Chen, Y. T., Burnett, E., Ali, S. A., Rayamajhi, A., Satter, S. M., Van Trang, N., Eibach, D., Krumkamp, R., May, J., Adegnika, A. A., Manouana, G. P., Kremsner, P. G., Chilengi, R., Hatyoka, L., Debes, A. K., Ateudjieu, J., Faruque, A. S. G., Hossain, M. J., Kanungo, S., Kotloff, K. L., Mandomando, I., Nisar, M. I., Omore, R., Sow, S. O., Zaidi, A. K. M., Lambrecht, N., Adu, B., Page, N., Platts-Mills, J. A., Mavacala Freitas, C., Pelkonen, T., Ashorn, P., Maleta, K., Ahmed, T., Bessong, P., Bhutta, Z. A., Mason, C., Mduma, E., Olortegui, M. P., Peñataro Yori, P., Lima, A. A. M., Kang, G., Humphrey, J., Ntozini, R., Prendergast, A. J., Okada, K., Wongboot, W., Langeland, N., Moyo, S. J., Gaensbauer, J., Melgar, M., Freeman, M., Chard, A. N., Thongpaseuth, V., Houpt, E., Zaitchik, B. F., Kosek, M. N. (2023): Spatiotemporal variation in risk of Shigella infection in childhood: a global risk mapping and prediction model using individual participant data. - The Lancet Global Health, 11, 3, e373-e384.
https://doi.org/10.1016/S2214-109X(22)00549-6

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 Creators:
Badr, Hamada S.1, Author
Colston, Josh M.1, Author
Nguyen, Nhat-Lan H.1, Author
Chen, Yen Ting1, Author
Burnett, Eleanor1, Author
Ali, Syed Asad1, Author
Rayamajhi, Ajit1, Author
Satter, Syed M.1, Author
Van Trang, Nguyen1, Author
Eibach, Daniel1, Author
Krumkamp, Ralf1, Author
May, Jürgen1, Author
Adegnika, Ayola Akim1, Author
Manouana, Gédéon Prince1, Author
Kremsner, Peter Gottfried1, Author
Chilengi, Roma1, Author
Hatyoka, Luiza1, Author
Debes, Amanda K.1, Author
Ateudjieu, Jerome1, Author
Faruque, Abu S. G.1, Author
Hossain, M Jahangir1, AuthorKanungo, Suman1, AuthorKotloff, Karen L.1, AuthorMandomando, Inácio1, AuthorNisar, M Imran1, AuthorOmore, Richard1, AuthorSow, Samba O.1, AuthorZaidi, Anita K. M.1, AuthorLambrecht, Nathalie2, Author              Adu, Bright1, AuthorPage, Nicola1, AuthorPlatts-Mills, James A.1, AuthorMavacala Freitas, Cesar1, AuthorPelkonen, Tuula1, AuthorAshorn, Per1, AuthorMaleta, Kenneth1, AuthorAhmed, Tahmeed1, AuthorBessong, Pascal1, AuthorBhutta, Zulfiqar A.1, AuthorMason, Carl1, AuthorMduma, Estomih1, AuthorOlortegui, Maribel P.1, AuthorPeñataro Yori, Pablo1, AuthorLima, Aldo A. M.1, AuthorKang, Gagandeep1, AuthorHumphrey, Jean1, AuthorNtozini, Robert1, AuthorPrendergast, Andrew J.1, AuthorOkada, Kazuhisa1, AuthorWongboot, Warawan1, AuthorLangeland, Nina1, AuthorMoyo, Sabrina J.1, AuthorGaensbauer, James1, AuthorMelgar, Mario1, AuthorFreeman, Matthew1, AuthorChard, Anna N.1, AuthorThongpaseuth, Vonethalom1, AuthorHoupt, Eric1, AuthorZaitchik, Benjamin F.1, AuthorKosek, Margaret N.1, Author more..
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, ou_persistent13              

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 Abstract: Background: 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.

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Language(s): eng - English
 Dates: 2022-08-022023-01-022023-02-142023-03
 Publication Status: Finally published
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1016/S2214-109X(22)00549-6
Organisational keyword: RD2 - Climate Resilience
PIKDOMAIN: RD2 - Climate Resilience
Working Group: Climate Change and Health
MDB-ID: No data to archive
Research topic keyword: Health
Regional keyword: Global
 Degree: -

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Title: The Lancet Global Health
Source Genre: Journal, SCI, Scopus, oa
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Pages: - Volume / Issue: 11 (3) Sequence Number: - Start / End Page: e373 - e384 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/the-lancet-global-health
Publisher: Elsevier