0 Recommendations

Malaria Surveillance: Use of spatial clustering

By Pierre Bush, PhD Moderator | 13 Apr, 2017

Dear Colleagues,
The use of GIS (Geographical Information System) to detect malaria clusters may be useful for surveillance purposes. The WHO used to help with this tool (see Gomez-Barroso et al., 2017, in their study conducted in Equatorial Guinea), but they abruptly stopped funding it. It could help to isolate the geographical areas prone to malaria, and help policymakers plan preventive measures accordingly.

Abstract

Background
The transmission of malaria is intense in the majority of the countries of sub-Saharan Africa, particularly in those that are located along the Equatorial strip. The present study aimed to describe the current distribution of malaria prevalence among children and its environment-related factors as well as to detect malaria spatial clusters in the district of Bata, in Equatorial Guinea.

Methods
From June to August 2013 a representative cross-sectional survey using a multistage, stratified, cluster-selected sample was carried out of children in urban and rural areas of Bata District. All children were tested for malaria using rapid diagnostic tests (RDTs). Results were linked to each household by global position system data. Two cluster analysis methods were used: hot spot analysis using the Getis-Ord Gi statistic, and the SaTScan™ spatial statistic estimates, based on the assumption of a Poisson distribution to detect spatial clusters. In addition, univariate associations and Poisson regression model were used to explore the association between malaria prevalence at household level with different environmental factors.

Results
A total of 1416 children aged 2 months to 15 years living in 417 households were included in this study. Malaria prevalence by RDTs was 47.53%, being highest in the age group 6–15 years (63.24%, p < 0.001). Those children living in rural areas were there malaria risk is greater (65.81%) (p < 0.001). Malaria prevalence was higher in those houses located <1 km from a river and <3 km to a forest (IRR: 1.31; 95% CI 1.13–1.51 and IRR: 1.44; 95% CI 1.25–1.66, respectively). Poisson regression analysis also showed a decrease in malaria prevalence with altitude (IRR: 0.73; 95% CI 0.62–0.86). A significant cluster inland of the district, in rural areas has been found.

Conclusions
This study reveals a high prevalence of RDT-based malaria among children in Bata district. Those households situated in inland rural areas, near to a river, a green area and/or at low altitude were a risk factor for malaria. Spatial tools can help policy makers to promote new recommendations for malaria control.

Attached resource:

Replies

 

Sungano Mharakurwa Moderator Replied at 5:03 PM, 14 Apr 2017

Thank you Pierre for sharing this helpful resource. Herewith another instrumental model in the course of elimination, though models may not always give the same answers!

Attached resource:

Peter Burgess Replied at 12:55 PM, 15 Apr 2017

OOPS ... this is what I intended to send !!!!!!!!!!!!!!!!!

Dear Dr. Bob

Happy Easter to you and your family.

I just came across this e-message and thought about you and my own interest
in the spatial dimension of development.

The issue of reduced funding also got my attention.

I am not getting any younger, but I continue to be very interested in
better metrics ... and while I am not well known, the work I am doing is
finding its way into some of the bigger initiatives to do a better job of
management metrics.

Exciting times

Peter
_____________________________
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Pierre Bush, PhD Moderator Replied at 4:50 PM, 15 Apr 2017

Hello Sungano &Peter,
Thank you for this additional resource ,and for responding to this discussion. More funding should be found for this tool which is very reliable and more practical than the malaria indicator & demographic surveys that are conducted every two years.

Dinesh Koirala Replied at 8:00 AM, 17 Apr 2017

Thank you.

Maimunat Alex-Adeomi Moderator Replied at 10:59 PM, 19 Apr 2017

Dear all,

Thank you for this informative article on the use of GIS for disease sampling.

I think like Peter, the abrupt stop to funding for this program by the WHO intrigued me and I actually wonder if politics or some higher level factors are responsible for this.

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