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1.
BMC Med ; 20(1): 28, 2022 01 27.
Artículo en Inglés | MEDLINE | ID: mdl-35081974

RESUMEN

BACKGROUND: Understanding the age patterns of disease is necessary to target interventions to maximise cost-effective impact. New malaria chemoprevention and vaccine initiatives target young children attending routine immunisation services. Here we explore the relationships between age and severity of malaria hospitalisation versus malaria transmission intensity. METHODS: Clinical data from 21 surveillance hospitals in East Africa were reviewed. Malaria admissions aged 1 month to 14 years from discrete administrative areas since 2006 were identified. Each site-time period was matched to a model estimated community-based age-corrected parasite prevalence to provide predictions of prevalence in childhood (PfPR2-10). Admission with all-cause malaria, severe malaria anaemia (SMA), respiratory distress (RD) and cerebral malaria (CM) were analysed as means and predicted probabilities from Bayesian generalised mixed models. RESULTS: 52,684 malaria admissions aged 1 month to 14 years were described at 21 hospitals from 49 site-time locations where PfPR2-10 varied from < 1 to 48.7%. Twelve site-time periods were described as low transmission (PfPR2-10 < 5%), five low-moderate transmission (PfPR2-10 5-9%), 20 moderate transmission (PfPR2-10 10-29%) and 12 high transmission (PfPR2-10 ≥ 30%). The majority of malaria admissions were below 5 years of age (69-85%) and rare among children aged 10-14 years (0.7-5.4%) across all transmission settings. The mean age of all-cause malaria hospitalisation was 49.5 months (95% CI 45.1, 55.4) under low transmission compared with 34.1 months (95% CI 30.4, 38.3) at high transmission, with similar trends for each severe malaria phenotype. CM presented among older children at a mean of 48.7 months compared with 39.0 months and 33.7 months for SMA and RD, respectively. In moderate and high transmission settings, 34% and 42% of the children were aged between 2 and 23 months and so within the age range targeted by chemoprevention or vaccines. CONCLUSIONS: Targeting chemoprevention or vaccination programmes to areas where community-based parasite prevalence is ≥10% is likely to match the age ranges covered by interventions (e.g. intermittent presumptive treatment in infancy to children aged 2-23 months and current vaccine age eligibility and duration of efficacy) and the age ranges of highest disease burden.


Asunto(s)
Malaria Cerebral , Malaria Falciparum , Adolescente , África Oriental/epidemiología , Teorema de Bayes , Niño , Preescolar , Hospitalización , Humanos , Lactante , Malaria Cerebral/epidemiología , Malaria Falciparum/epidemiología , Fenotipo
2.
Science ; 373(6557): 926-931, 2021 08 20.
Artículo en Inglés | MEDLINE | ID: mdl-34413238

RESUMEN

The relationship between community prevalence of Plasmodium falciparum and the burden of severe, life-threatening disease remains poorly defined. To examine the three most common severe malaria phenotypes from catchment populations across East Africa, we assembled a dataset of 6506 hospital admissions for malaria in children aged 3 months to 9 years from 2006 to 2020. Admissions were paired with data from community parasite infection surveys. A Bayesian procedure was used to calibrate uncertainties in exposure (parasite prevalence) and outcomes (severe malaria phenotypes). Each 25% increase in prevalence conferred a doubling of severe malaria admission rates. Severe malaria remains a burden predominantly among young children (3 to 59 months) across a wide range of community prevalence typical of East Africa. This study offers a quantitative framework for linking malaria parasite prevalence and severe disease outcomes in children.


Asunto(s)
Malaria Falciparum/epidemiología , Plasmodium falciparum , África Oriental/epidemiología , Factores de Edad , Teorema de Bayes , Niño , Preescolar , Monitoreo Epidemiológico , Hospitalización , Humanos , Incidencia , Lactante , Malaria Cerebral/epidemiología , Malaria Falciparum/prevención & control , Malaria Falciparum/transmisión , Modelos Estadísticos , Prevalencia , Factores de Riesgo , Índice de Severidad de la Enfermedad
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