Bayesian spatial modelling of malaria burden in two contrasted eco-epidemiological facies in Benin (West Africa): call for localized interventions.


Journal

BMC public health
ISSN: 1471-2458
Titre abrégé: BMC Public Health
Pays: England
ID NLM: 100968562

Informations de publication

Date de publication:
16 09 2022
Historique:
received: 30 04 2022
accepted: 16 08 2022
entrez: 16 9 2022
pubmed: 17 9 2022
medline: 21 9 2022
Statut: epublish

Résumé

Despite a global decrease in malaria burden worldwide, malaria remains a major public health concern, especially in Benin children, the most vulnerable group. A better understanding of malaria's spatial and age-dependent characteristics can help provide durable disease control and elimination. This study aimed to analyze the spatial distribution of Plasmodium falciparum malaria infection and disease among children under five years of age in Benin, West Africa. A cross-sectional epidemiological and clinical survey was conducted using parasitological examination and rapid diagnostic tests (RDT) in Benin. Interviews were done with 10,367 children from 72 villages across two health districts in Benin. The prevalence of infection and clinical cases was estimated according to age. A Bayesian spatial binomial model was used to estimate the prevalence of malaria infection, and clinical cases were adjusted for environmental and demographic covariates. It was implemented in R using Integrated Nested Laplace Approximations (INLA) and Stochastic Partial Differentiation Equations (SPDE) techniques. The prevalence of P. falciparum infection was moderate in the south (34.6%) of Benin and high in the northern region (77.5%). In the south, the prevalence of P. falciparum infection and clinical malaria cases were similar according to age. In northern Benin children under six months of age were less frequently infected than children aged 6-11, 12-23, 24-60 months, (p < 0.0001) and had the lowest risk of malaria cases compared to the other age groups (6-12), (13-23) and (24-60): OR = 3.66 [2.21-6.05], OR = 3.66 [2.21-6.04], and OR = 2.83 [1.77-4.54] respectively (p < 0.0001). Spatial model prediction showed more heterogeneity in the south than in the north but a higher risk of malaria infection and clinical cases in the north than in the south. Integrated and periodic risk mapping of Plasmodium falciparum infection and clinical cases will make interventions more evidence-based by showing progress or a lack in malaria control.

Sections du résumé

BACKGROUND
Despite a global decrease in malaria burden worldwide, malaria remains a major public health concern, especially in Benin children, the most vulnerable group. A better understanding of malaria's spatial and age-dependent characteristics can help provide durable disease control and elimination. This study aimed to analyze the spatial distribution of Plasmodium falciparum malaria infection and disease among children under five years of age in Benin, West Africa.
METHODS
A cross-sectional epidemiological and clinical survey was conducted using parasitological examination and rapid diagnostic tests (RDT) in Benin. Interviews were done with 10,367 children from 72 villages across two health districts in Benin. The prevalence of infection and clinical cases was estimated according to age. A Bayesian spatial binomial model was used to estimate the prevalence of malaria infection, and clinical cases were adjusted for environmental and demographic covariates. It was implemented in R using Integrated Nested Laplace Approximations (INLA) and Stochastic Partial Differentiation Equations (SPDE) techniques.
RESULTS
The prevalence of P. falciparum infection was moderate in the south (34.6%) of Benin and high in the northern region (77.5%). In the south, the prevalence of P. falciparum infection and clinical malaria cases were similar according to age. In northern Benin children under six months of age were less frequently infected than children aged 6-11, 12-23, 24-60 months, (p < 0.0001) and had the lowest risk of malaria cases compared to the other age groups (6-12), (13-23) and (24-60): OR = 3.66 [2.21-6.05], OR = 3.66 [2.21-6.04], and OR = 2.83 [1.77-4.54] respectively (p < 0.0001). Spatial model prediction showed more heterogeneity in the south than in the north but a higher risk of malaria infection and clinical cases in the north than in the south.
CONCLUSION
Integrated and periodic risk mapping of Plasmodium falciparum infection and clinical cases will make interventions more evidence-based by showing progress or a lack in malaria control.

Identifiants

pubmed: 36114483
doi: 10.1186/s12889-022-14032-9
pii: 10.1186/s12889-022-14032-9
pmc: PMC9479262
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

1754

Informations de copyright

© 2022. The Author(s).

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Auteurs

Barikissou Georgia Damien (BG)

Centre de Recherche Entomologique de Cotonou, Ministère de la Santé, Cotonou, Benin. barikiss2000@yahoo.fr.
Population Research Center (CEFORP), University of Abomey-Calavi, Cotonou, Bénin. barikiss2000@yahoo.fr.
MIVEGEC (Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Evolution Et Contrôle), University of Montpellier, IRD, CNRS, Montpellier, France. barikiss2000@yahoo.fr.

Akoeugnigan Idelphonse Sode (AI)

Laboratoire de Biomathématiques et d'Estimations Forestières, Université d'Abomey-Calavi, Abomey-Calavi, Benin.
Pôle des Technlogies de l'Information et de la Communication pour le Développement (ICT4D), IT4LIFE, Dakar, Sénégal.

Daniel Bocossa (D)

Universités Paris 8, UFR Etudes - Recherche - et Ingénierie en territoires - Environnements - Société, Saint-Denis, France.

Emmanuel Elanga-Ndille (E)

Departement of Medical Entomology, Centre for Research in Infectious Diseases (CRID), P.O. BOX 13591, Yaoundé, Cameroon.
Department of Animal Biology, Faculty of Science of the University of Dschang, Vector Borne Diseases Laboratory of the Biology and Applied Ecology Research Unit (VBID-URBEA), Dschang, Cameroon.

Badirou Aguemon (B)

Departement de Santé Publique, Faculté des Sciences de la Santé, Université d'Abomey-Calavi, Cotonou, Benin.

Vincent Corbel (V)

MIVEGEC (Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Evolution Et Contrôle), University of Montpellier, IRD, CNRS, Montpellier, France.

Marie-Claire Henry (MC)

Centre de Recherche Entomologique de Cotonou, Ministère de la Santé, Cotonou, Benin.

Romain Lucas Glèlè Kakaï (RL)

Laboratoire de Biomathématiques et d'Estimations Forestières, Université d'Abomey-Calavi, Abomey-Calavi, Benin.

Franck Remoué (F)

MIVEGEC (Maladies Infectieuses et Vecteurs: Ecologie, Génétique, Evolution Et Contrôle), University of Montpellier, IRD, CNRS, Montpellier, France.

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