Severe outcomes of malaria in children under time-varying exposure.

Autor: De Salazar PM; Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland. pablo.martinezdesalazar@swisstph.ch.; University of Basel, Basel, Switzerland. pablo.martinezdesalazar@swisstph.ch., Kamau A; Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Nairobi, Kenya., Cavelan A; Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland.; University of Basel, Basel, Switzerland., Akech S; Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Nairobi, Kenya., Mpimbaza A; Child Health and Development Centre, College of Health Sciences, Makerere University, Kampala, Uganda., Snow RW; Kenya Medical Research Institute (KEMRI)-Wellcome Trust Research Programme, Nairobi, Kenya.; Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Oxford, UK., Penny MA; Department of Epidemiology and Public Health, Swiss Tropical and Public Health Institute, Allschwil, Switzerland. melissa.penny@uwa.edu.au.; University of Basel, Basel, Switzerland. melissa.penny@uwa.edu.au.; Telethon Kids Institute, Nedlands, WA, Australia. melissa.penny@uwa.edu.au.; Centre for Child Health Research, University of Western Australia, Crawley, WA, Australia. melissa.penny@uwa.edu.au.
Jazyk: angličtina
Zdroj: Nature communications [Nat Commun] 2024 May 14; Vol. 15 (1), pp. 4069. Date of Electronic Publication: 2024 May 14.
DOI: 10.1038/s41467-024-48191-7
Abstrakt: In malaria epidemiology, interpolation frameworks based on available observations are critical for policy decisions and interpreting disease burden. Updating our understanding of the empirical evidence across different populations, settings, and timeframes is crucial to improving inference for supporting public health. Here, via individual-based modeling, we evaluate a large, multicountry, contemporary Plasmodium falciparum severe malaria dataset to better understand the relationship between prevalence and incidence of malaria pediatric hospitalizations - a proxy of malaria severe outcomes- in East-Africa. We find that life-long exposure dynamics, and subsequent protection patterns in children, substantially determine the likelihood of malaria hospitalizations relative to ongoing prevalence at the population level. Unsteady transmission patterns over a lifetime in children -increasing or decreasing- lead to an exponential relationship of hospitalization rates versus prevalence rather than the asymptotic pattern observed under steady transmission. Addressing this increase in the complexity of malaria epidemiology is crucial to update burden assessments via inference models that guide current and future policy decisions.
(© 2024. The Author(s).)
Databáze: MEDLINE
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