Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi

Kabaghe, Alinune N, Chipeta, Michael G, McCann, Robert S, Phiri, Kamija S, van Vugt, Michele, Takken, Willem, Diggle, Peter and Terlouw, Anja ORCID: https://orcid.org/0000-0001-5327-8995 (2017) 'Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi'. PLoS ONE, Vol 12, Issue 2, e0172266.

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Abstract

Introduction
In the context of malaria elimination, interventions will need to target high burden areas to further reduce transmission. Current tools to monitor and report disease burden lack the capacity to continuously detect fine-scale spatial and temporal variations of disease distribution exhibited by malaria. These tools use random sampling techniques that are inefficient for capturing underlying heterogeneity while health facility data in resource-limited settings are inaccurate. Continuous community surveys of malaria burden provide real-time results of local spatio-temporal variation. Adaptive geostatistical design (AGD) improves prediction of outcome of interest compared to current random sampling techniques. We present findings of continuous malaria prevalence surveys using an adaptive sampling design.

Methods
We conducted repeated cross sectional surveys guided by an adaptive sampling design to monitor the prevalence of malaria parasitaemia and anaemia in children below five years old in the communities living around Majete Wildlife Reserve in Chikwawa district, Southern Malawi. AGD sampling uses previously collected data to sample new locations of high prediction variance or, where prediction exceeds a set threshold. We fitted a geostatistical model to predict malaria prevalence in the area.

Findings
We conducted five rounds of sampling, and tested 876 children aged 6-59 months from 1377 households over a 12-month period. Malaria prevalence prediction maps showed spatial heterogeneity and presence of hotspots – where predicted malaria prevalence was above 30%; predictors of malaria included age, socio-economic status and ownership of insecticide-treated mosquito nets.

Conclusions
Continuous malaria prevalence surveys using adaptive sampling increased malaria prevalence prediction accuracy. Results from the surveys were readily available after data collection.
The tool can assist local managers to target malaria control interventions in areas with the greatest health impact and is ready for assessment in other diseases.

Item Type:	Article
Uncontrolled Keywords:	NOT_LSTM
Subjects:	QS Anatomy > QS 4 General works. Classify here works on regional anatomy WA Public Health > WA 20.5 Research (General) WA Public Health > WA 30 Socioeconomic factors in public health (General) WA Public Health > Health Problems of Special Population Groups > WA 320 Child Welfare. Child Health Services. WA Public Health > Health Problems of Special Population Groups > WA 395 Health in developing countries WA Public Health > Statistics. Surveys > WA 900 Public health statistics WC Communicable Diseases > Tropical and Parasitic Diseases > WC 750 Malaria
Faculty: Department:	Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI):	https://doi.org/10.1371/journal.pone.0172266
Depositing User:	Stacy Murtagh
Date Deposited:	06 Jun 2017 11:03
Last Modified:	05 Nov 2024 10:50
URI:	https://archive.lstmed.ac.uk/id/eprint/6834

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