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Malaria prevalence metrics in low- and middle-income countries: An assessment of precision in nationally representative surveys

Malaria prevalence metrics in low- and middle-income countries: An assessment of precision in nationally representative surveys
Malaria prevalence metrics in low- and middle-income countries: An assessment of precision in nationally representative surveys
Background: One pillar to monitoring progress towards the Sustainable Development Goals is the investment in high quality data to strengthen the scientific basis for decision-making. At present, nationally-representative surveys are the main source of data for establishing a scientific evidence base, monitoring, and evaluation of health metrics. However, little is known about the optimal precisions of various population-level health and development indicators that remains unquantified in nationally-representative household surveys. Here, a retrospective analysis of the precision of prevalence from these surveys was conducted.

Methods: Using malaria indicators, data were assembled in nine sub-Saharan African countries with at least two nationally-representative surveys. A Bayesian statistical model was used to estimate between- and within-cluster variability for fever and malaria prevalence, and insecticide-treated bed nets (ITNs) use in children under the age of five years. The intra-class correlation coefficient was estimated along with the optimal sample size for each indicator with associated uncertainty.

Findings: Results suggest that the estimated sample sizes for the current nationally representative surveys increases with declining malaria prevalence. Comparison between the actual sample size and the modelled estimate showed a requirement to
increase the sample size for parasite prevalence by up to 77.7% (95% Bayesian credible intervals 74.7-79.4) for the 2015 Kenya MIS (estimated sample size of children 0-4 years 7,218 [7,099-7,288]), and 54.1% [50.1-56.5] for the 014-2015 Rwanda DHS (12,220 [11,950-12,410]).

Conclusion: This study highlights the importance of defining indicator-relevant sample sizes to achieve the required precision in the current national surveys. While expanding the current surveys would need additional investment, the study highlights the need for improved approaches to cost effective sampling.
1475-2875
Alegana, Victor
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Wright, Jim
94990ecf-f8dd-4649-84f2-b28bf272e464
Bosco, Claudio
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Okiro, Emelda A.
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Atkinson, Peter M.
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Snow, Robert W.
7ff98228-6657-4b33-9793-b7f91a06c187
Tatem, Andrew
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Noor, Abdisalan M.
06d32991-29fe-47a5-a62b-fe584c753414
Alegana, Victor
f5bd6ab7-459e-4122-984f-2bdb5f906d82
Wright, Jim
94990ecf-f8dd-4649-84f2-b28bf272e464
Bosco, Claudio
9bf27082-5f4c-4b9f-8f12-6c4159f556f5
Okiro, Emelda A.
24e6f33b-9321-4888-adfc-3380911e1dac
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Snow, Robert W.
7ff98228-6657-4b33-9793-b7f91a06c187
Tatem, Andrew
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Noor, Abdisalan M.
06d32991-29fe-47a5-a62b-fe584c753414

Alegana, Victor, Wright, Jim, Bosco, Claudio, Okiro, Emelda A., Atkinson, Peter M., Snow, Robert W., Tatem, Andrew and Noor, Abdisalan M. (2017) Malaria prevalence metrics in low- and middle-income countries: An assessment of precision in nationally representative surveys. Malaria Journal, 16 (475). (doi:10.1186/s12936-017-2127-y).

Record type: Article

Abstract

Background: One pillar to monitoring progress towards the Sustainable Development Goals is the investment in high quality data to strengthen the scientific basis for decision-making. At present, nationally-representative surveys are the main source of data for establishing a scientific evidence base, monitoring, and evaluation of health metrics. However, little is known about the optimal precisions of various population-level health and development indicators that remains unquantified in nationally-representative household surveys. Here, a retrospective analysis of the precision of prevalence from these surveys was conducted.

Methods: Using malaria indicators, data were assembled in nine sub-Saharan African countries with at least two nationally-representative surveys. A Bayesian statistical model was used to estimate between- and within-cluster variability for fever and malaria prevalence, and insecticide-treated bed nets (ITNs) use in children under the age of five years. The intra-class correlation coefficient was estimated along with the optimal sample size for each indicator with associated uncertainty.

Findings: Results suggest that the estimated sample sizes for the current nationally representative surveys increases with declining malaria prevalence. Comparison between the actual sample size and the modelled estimate showed a requirement to
increase the sample size for parasite prevalence by up to 77.7% (95% Bayesian credible intervals 74.7-79.4) for the 2015 Kenya MIS (estimated sample size of children 0-4 years 7,218 [7,099-7,288]), and 54.1% [50.1-56.5] for the 014-2015 Rwanda DHS (12,220 [11,950-12,410]).

Conclusion: This study highlights the importance of defining indicator-relevant sample sizes to achieve the required precision in the current national surveys. While expanding the current surveys would need additional investment, the study highlights the need for improved approaches to cost effective sampling.

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Accepted/In Press date: 16 November 2017
e-pub ahead of print date: 21 November 2017

Identifiers

Local EPrints ID: 417941
URI: http://eprints.soton.ac.uk/id/eprint/417941
ISSN: 1475-2875
PURE UUID: 8e7ed683-0455-49d2-8245-66135be8a7fb
ORCID for Victor Alegana: ORCID iD orcid.org/0000-0001-5177-9227
ORCID for Jim Wright: ORCID iD orcid.org/0000-0002-8842-2181
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for Andrew Tatem: ORCID iD orcid.org/0000-0002-7270-941X

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Date deposited: 17 Feb 2018 17:30
Last modified: 10 Dec 2019 01:55

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Contributors

Author: Victor Alegana ORCID iD
Author: Jim Wright ORCID iD
Author: Claudio Bosco
Author: Emelda A. Okiro
Author: Peter M. Atkinson ORCID iD
Author: Robert W. Snow
Author: Andrew Tatem ORCID iD
Author: Abdisalan M. Noor

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