Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys
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 evide...
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ftdoajarticles:oai:doaj.org/article:d8be31b30a8b465cb181dbe1e670043c 2023-05-15T15:15:55+02:00 Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys Victor A. Alegana Jim Wright Claudio Bosco Emelda A. Okiro Peter M. Atkinson Robert W. Snow Andrew J. Tatem Abdisalan M. Noor 2017-11-01T00:00:00Z https://doi.org/10.1186/s12936-017-2127-y https://doaj.org/article/d8be31b30a8b465cb181dbe1e670043c EN eng BMC http://link.springer.com/article/10.1186/s12936-017-2127-y https://doaj.org/toc/1475-2875 doi:10.1186/s12936-017-2127-y 1475-2875 https://doaj.org/article/d8be31b30a8b465cb181dbe1e670043c Malaria Journal, Vol 16, Iss 1, Pp 1-11 (2017) Indicators Intra-class correlation Malaria Precision Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 article 2017 ftdoajarticles https://doi.org/10.1186/s12936-017-2127-y 2022-12-31T15:01:49Z 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 5 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 7218 [7099–7288]), and 54.1% [50.1–56.5] for the 2014–2015 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. Article in Journal/Newspaper Arctic Directory of Open Access Journals: DOAJ Articles Arctic Pillar ENVELOPE(166.217,166.217,-77.583,-77.583) Malaria Journal 16 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Indicators Intra-class correlation Malaria Precision Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
spellingShingle |
Indicators Intra-class correlation Malaria Precision Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 Victor A. Alegana Jim Wright Claudio Bosco Emelda A. Okiro Peter M. Atkinson Robert W. Snow Andrew J. Tatem Abdisalan M. Noor Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys |
topic_facet |
Indicators Intra-class correlation Malaria Precision Arctic medicine. Tropical medicine RC955-962 Infectious and parasitic diseases RC109-216 |
description |
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 5 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 7218 [7099–7288]), and 54.1% [50.1–56.5] for the 2014–2015 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. |
format |
Article in Journal/Newspaper |
author |
Victor A. Alegana Jim Wright Claudio Bosco Emelda A. Okiro Peter M. Atkinson Robert W. Snow Andrew J. Tatem Abdisalan M. Noor |
author_facet |
Victor A. Alegana Jim Wright Claudio Bosco Emelda A. Okiro Peter M. Atkinson Robert W. Snow Andrew J. Tatem Abdisalan M. Noor |
author_sort |
Victor A. Alegana |
title |
Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys |
title_short |
Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys |
title_full |
Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys |
title_fullStr |
Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys |
title_full_unstemmed |
Malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys |
title_sort |
malaria prevalence metrics in low- and middle-income countries: an assessment of precision in nationally-representative surveys |
publisher |
BMC |
publishDate |
2017 |
url |
https://doi.org/10.1186/s12936-017-2127-y https://doaj.org/article/d8be31b30a8b465cb181dbe1e670043c |
long_lat |
ENVELOPE(166.217,166.217,-77.583,-77.583) |
geographic |
Arctic Pillar |
geographic_facet |
Arctic Pillar |
genre |
Arctic |
genre_facet |
Arctic |
op_source |
Malaria Journal, Vol 16, Iss 1, Pp 1-11 (2017) |
op_relation |
http://link.springer.com/article/10.1186/s12936-017-2127-y https://doaj.org/toc/1475-2875 doi:10.1186/s12936-017-2127-y 1475-2875 https://doaj.org/article/d8be31b30a8b465cb181dbe1e670043c |
op_doi |
https://doi.org/10.1186/s12936-017-2127-y |
container_title |
Malaria Journal |
container_volume |
16 |
container_issue |
1 |
_version_ |
1766346250781720576 |