The University of Southampton
University of Southampton Institutional Repository

Bayesian spatiotemporal modeling of routinely collected data to assess the effect of health programs in malaria incidence during pregnancy in Burkina Faso

Bayesian spatiotemporal modeling of routinely collected data to assess the effect of health programs in malaria incidence during pregnancy in Burkina Faso
Bayesian spatiotemporal modeling of routinely collected data to assess the effect of health programs in malaria incidence during pregnancy in Burkina Faso
Control of malaria in pregnancy (MiP) remains a major challenge in Burkina Faso. Surveillance of the burden due to MiP based on routinely collected data at a fine-scale level, followed by an appropriate analysis and interpretation, may be crucial for evaluating and improving the effectiveness of existing control measures. We described the spatio-temporal dynamics of MiP at the community-level and assessed health program effects, mainly community-based health promotion, results-based financing, and intermittent-preventive-treatment with sulphadoxine-pyrimethamine (IPTp-SP). Community-aggregated monthly MiP cases were downloaded from Health Management Information System and combined with covariates from other sources. The MiP spatio-temporal pattern was decomposed into three components: overall spatial and temporal trends and space-time interaction. Bayesian hierarchical spatio-temporal Poisson models were used to fit the MiP incidence rate and assess health program effects. The overall annual incidence increased between 2015 and 2017. The findings reveal spatio-temporal heterogenicity throughout the year, which peaked during rainy season. From the model without covariates, 96 communities located mainly in the Cascades, South-West, Center-West, Center-East, and Eastern regions, exhibited significant relative-risk levels. The combined effect (significant reducing effect) of RBF, health promotion and IPTp-SP strategies was greatest in 17.7% (17/96) of high burden malaria communities. Despite intensification of control efforts, MiP remains high at the community-scale. The provided risk maps are useful tools for highlighting areas where interventions should be optimized, particularly in high-risk communities.
2045-2322
Rouamba, Toussaint
6c1b9f83-cf67-4594-96a6-ab29fd0af593
Samadoulougou, Sekou
acd5e8cf-d12a-4294-80e4-a87c6e23d2de
Tinto, Halidou
8f149b48-e65f-4ae7-9e4e-5d7923e61c85
Alegana, Victor A.
f5bd6ab7-459e-4122-984f-2bdb5f906d82
Kirakoya-Samadoulougou, Fati
7198d2b2-fc56-4251-a9b6-3fe512c105a5
Rouamba, Toussaint
6c1b9f83-cf67-4594-96a6-ab29fd0af593
Samadoulougou, Sekou
acd5e8cf-d12a-4294-80e4-a87c6e23d2de
Tinto, Halidou
8f149b48-e65f-4ae7-9e4e-5d7923e61c85
Alegana, Victor A.
f5bd6ab7-459e-4122-984f-2bdb5f906d82
Kirakoya-Samadoulougou, Fati
7198d2b2-fc56-4251-a9b6-3fe512c105a5

Rouamba, Toussaint, Samadoulougou, Sekou, Tinto, Halidou, Alegana, Victor A. and Kirakoya-Samadoulougou, Fati (2020) Bayesian spatiotemporal modeling of routinely collected data to assess the effect of health programs in malaria incidence during pregnancy in Burkina Faso. Scientific Reports, 10 (1), [2618]. (doi:10.1038/s41598-020-58899-3).

Record type: Article

Abstract

Control of malaria in pregnancy (MiP) remains a major challenge in Burkina Faso. Surveillance of the burden due to MiP based on routinely collected data at a fine-scale level, followed by an appropriate analysis and interpretation, may be crucial for evaluating and improving the effectiveness of existing control measures. We described the spatio-temporal dynamics of MiP at the community-level and assessed health program effects, mainly community-based health promotion, results-based financing, and intermittent-preventive-treatment with sulphadoxine-pyrimethamine (IPTp-SP). Community-aggregated monthly MiP cases were downloaded from Health Management Information System and combined with covariates from other sources. The MiP spatio-temporal pattern was decomposed into three components: overall spatial and temporal trends and space-time interaction. Bayesian hierarchical spatio-temporal Poisson models were used to fit the MiP incidence rate and assess health program effects. The overall annual incidence increased between 2015 and 2017. The findings reveal spatio-temporal heterogenicity throughout the year, which peaked during rainy season. From the model without covariates, 96 communities located mainly in the Cascades, South-West, Center-West, Center-East, and Eastern regions, exhibited significant relative-risk levels. The combined effect (significant reducing effect) of RBF, health promotion and IPTp-SP strategies was greatest in 17.7% (17/96) of high burden malaria communities. Despite intensification of control efforts, MiP remains high at the community-scale. The provided risk maps are useful tools for highlighting areas where interventions should be optimized, particularly in high-risk communities.

Text
s41598-020-58899-3 - Version of Record
Available under License Creative Commons Attribution.
Download (50MB)

More information

Accepted/In Press date: 19 January 2020
e-pub ahead of print date: 14 February 2020

Identifiers

Local EPrints ID: 438161
URI: http://eprints.soton.ac.uk/id/eprint/438161
ISSN: 2045-2322
PURE UUID: 1ee74823-4dcf-4412-a97f-e0a0c86bf9eb
ORCID for Victor A. Alegana: ORCID iD orcid.org/0000-0001-5177-9227

Catalogue record

Date deposited: 03 Mar 2020 17:44
Last modified: 07 Oct 2020 02:06

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×