Using ante-natal clinic prevalence data to monitor temporal changes in malaria incidence in a humanitarian setting in the Democratic Republic of Congo
File(s)
Published version
Author(s)
Hellewell, J
Walker, Patrick
Ghani, Azra
Rao, Bhargavi V
Churcher, Thomas
Type
Journal Article
Abstract
Background
The number of clinical cases of malaria is often recorded in resource constrained or conflict settings as a proxy for disease burden. Interpreting case count data in areas of humanitarian need is challenging due to uncertainties in population size caused by security concerns, resource constraints and population movement. Malaria prevalence in women visiting ante-natal care (ANC) clinics has the potential to be an easier and more accurate metric for malaria surveillance that is unbiased by population size if malaria testing is routinely conducted irrespective of symptoms.
Methods
A suite of distributed lag non-linear models was fitted to clinical incidence time-series data in children under 5 years and ANC prevalence data from health centres run by Médecins Sans Frontières in the Democratic Republic of Congo, which implement routine intermittent screening and treatment (IST) alongside intermittent preventative treatment in pregnancy (IPTp). These statistical models enable the temporal relationship between the two metrics to be disentangled.
Results
There was a strong relationship between the ANC prevalence and clinical incidence suggesting that both can be used to describe current malaria endemicity. There was no evidence that ANC prevalence could predict future clinical incidence, though a change in clinical incidence was shown to influence ANC prevalence up to 3 months into the future.
Conclusions
The results indicate that ANC prevalence may be a suitable metric for retrospective evaluations of the impact of malaria interventions and is a useful method for evaluating long-term malaria trends in resource constrained settings.
The number of clinical cases of malaria is often recorded in resource constrained or conflict settings as a proxy for disease burden. Interpreting case count data in areas of humanitarian need is challenging due to uncertainties in population size caused by security concerns, resource constraints and population movement. Malaria prevalence in women visiting ante-natal care (ANC) clinics has the potential to be an easier and more accurate metric for malaria surveillance that is unbiased by population size if malaria testing is routinely conducted irrespective of symptoms.
Methods
A suite of distributed lag non-linear models was fitted to clinical incidence time-series data in children under 5 years and ANC prevalence data from health centres run by Médecins Sans Frontières in the Democratic Republic of Congo, which implement routine intermittent screening and treatment (IST) alongside intermittent preventative treatment in pregnancy (IPTp). These statistical models enable the temporal relationship between the two metrics to be disentangled.
Results
There was a strong relationship between the ANC prevalence and clinical incidence suggesting that both can be used to describe current malaria endemicity. There was no evidence that ANC prevalence could predict future clinical incidence, though a change in clinical incidence was shown to influence ANC prevalence up to 3 months into the future.
Conclusions
The results indicate that ANC prevalence may be a suitable metric for retrospective evaluations of the impact of malaria interventions and is a useful method for evaluating long-term malaria trends in resource constrained settings.
Date Issued
2018-08-29
Date Acceptance
2018-08-17
Citation
Malaria Journal, 2018, 17 (1)
ISSN
1475-2875
Publisher
BioMed Central
Journal / Book Title
Malaria Journal
Volume
17
Issue
1
Copyright Statement
© 2018 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Sponsor
Wellcome Trust
Wellcome Trust
Grant Number
105272/Z/14/Z
105272/Z/14/Z
Subjects
Science & Technology
Life Sciences & Biomedicine
Infectious Diseases
Parasitology
Tropical Medicine
Plasmodium falciparum
Malaria in pregnancy
Epidemiology
LAG NONLINEAR MODELS
PLASMODIUM-FALCIPARUM
TRANSMISSION
POPULATION
AFRICA
IMPACT
1108 Medical Microbiology
Publication Status
Published
Article Number
312
Date Publish Online
2018-08-29