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Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches

BACKGROUND: Threats to maintaining high population access with effective bed nets persist due to errors in quantification, bed net wear and tear, and inefficiencies in distribution activities. Monitoring bed net coverage is therefore critical, but usually occurs every 2–3 years through expensive, la...

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Autores principales: Worges, Matt, Kamala, Benjamin, Yukich, Joshua, Chacky, Frank, Lazaro, Samwel, Dismas, Charles, Aroun, Sijenun, Ibrahim, Raya, Khamis, Mwinyi, Gitanya, Mponeja P., Mwingizi, Deodatus, Metcalfe, Hannah, Bantanuka, Willhard, Deku, Sena, Dadi, David, Serbantez, Naomi, Loll, Dana, Koenker, Hannah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735037/
https://www.ncbi.nlm.nih.gov/pubmed/36496423
http://dx.doi.org/10.1186/s12936-022-04408-y
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author Worges, Matt
Kamala, Benjamin
Yukich, Joshua
Chacky, Frank
Lazaro, Samwel
Dismas, Charles
Aroun, Sijenun
Ibrahim, Raya
Khamis, Mwinyi
Gitanya, Mponeja P.
Mwingizi, Deodatus
Metcalfe, Hannah
Bantanuka, Willhard
Deku, Sena
Dadi, David
Serbantez, Naomi
Loll, Dana
Koenker, Hannah
author_facet Worges, Matt
Kamala, Benjamin
Yukich, Joshua
Chacky, Frank
Lazaro, Samwel
Dismas, Charles
Aroun, Sijenun
Ibrahim, Raya
Khamis, Mwinyi
Gitanya, Mponeja P.
Mwingizi, Deodatus
Metcalfe, Hannah
Bantanuka, Willhard
Deku, Sena
Dadi, David
Serbantez, Naomi
Loll, Dana
Koenker, Hannah
author_sort Worges, Matt
collection PubMed
description BACKGROUND: Threats to maintaining high population access with effective bed nets persist due to errors in quantification, bed net wear and tear, and inefficiencies in distribution activities. Monitoring bed net coverage is therefore critical, but usually occurs every 2–3 years through expensive, large-scale household surveys. Mobile phone-based survey methodologies are emerging as an alternative to household surveys and can provide rapid estimates of coverage, however, little research on varied sampling approaches has been conducted in sub-Saharan Africa. METHODS: A nationally and regionally representative cross-sectional mobile phone survey was conducted in early 2021 in Tanzania with focus on bed net ownership and access. Half the target sample was contacted through a random digit dial methodology (n = 3500) and the remaining half was reached through a voluntary opt-in respondent pool (n = 3500). Both sampling approaches used an interactive voice response survey. Standard RBM-MERG bed net indicators and AAPOR call metrics were calculated. In addition, the results of the two sampling approaches were compared. RESULTS: Population access (i.e., the percent of the population that could sleep under a bed net, assuming one bed net per two people) varied from a regionally adjusted low of 48.1% (Katavi) to a high of 65.5% (Dodoma). The adjusted percent of households that had a least one bed net ranged from 54.8% (Pemba) to 75.5% (Dodoma); the adjusted percent of households with at least one bed net per 2 de facto household population ranged from 35.9% (Manyara) to 55.7% (Dodoma). The estimates produced by both sampling approaches were generally similar, differing by only a few percentage points. An analysis of differences between estimates generated from the two sampling approaches showed minimal bias when considering variation across the indicator for households with at least one bed net per two de facto household population. CONCLUSION: The results generated by this survey show that overall bed net access in the country appears to be lower than target thresholds. The results suggest that bed net distribution is needed in large sections of the country to ensure that coverage levels remain high enough to sustain protection against malaria for the population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-022-04408-y.
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spelling pubmed-97350372022-12-11 Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches Worges, Matt Kamala, Benjamin Yukich, Joshua Chacky, Frank Lazaro, Samwel Dismas, Charles Aroun, Sijenun Ibrahim, Raya Khamis, Mwinyi Gitanya, Mponeja P. Mwingizi, Deodatus Metcalfe, Hannah Bantanuka, Willhard Deku, Sena Dadi, David Serbantez, Naomi Loll, Dana Koenker, Hannah Malar J Research BACKGROUND: Threats to maintaining high population access with effective bed nets persist due to errors in quantification, bed net wear and tear, and inefficiencies in distribution activities. Monitoring bed net coverage is therefore critical, but usually occurs every 2–3 years through expensive, large-scale household surveys. Mobile phone-based survey methodologies are emerging as an alternative to household surveys and can provide rapid estimates of coverage, however, little research on varied sampling approaches has been conducted in sub-Saharan Africa. METHODS: A nationally and regionally representative cross-sectional mobile phone survey was conducted in early 2021 in Tanzania with focus on bed net ownership and access. Half the target sample was contacted through a random digit dial methodology (n = 3500) and the remaining half was reached through a voluntary opt-in respondent pool (n = 3500). Both sampling approaches used an interactive voice response survey. Standard RBM-MERG bed net indicators and AAPOR call metrics were calculated. In addition, the results of the two sampling approaches were compared. RESULTS: Population access (i.e., the percent of the population that could sleep under a bed net, assuming one bed net per two people) varied from a regionally adjusted low of 48.1% (Katavi) to a high of 65.5% (Dodoma). The adjusted percent of households that had a least one bed net ranged from 54.8% (Pemba) to 75.5% (Dodoma); the adjusted percent of households with at least one bed net per 2 de facto household population ranged from 35.9% (Manyara) to 55.7% (Dodoma). The estimates produced by both sampling approaches were generally similar, differing by only a few percentage points. An analysis of differences between estimates generated from the two sampling approaches showed minimal bias when considering variation across the indicator for households with at least one bed net per two de facto household population. CONCLUSION: The results generated by this survey show that overall bed net access in the country appears to be lower than target thresholds. The results suggest that bed net distribution is needed in large sections of the country to ensure that coverage levels remain high enough to sustain protection against malaria for the population. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12936-022-04408-y. BioMed Central 2022-12-10 /pmc/articles/PMC9735037/ /pubmed/36496423 http://dx.doi.org/10.1186/s12936-022-04408-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Worges, Matt
Kamala, Benjamin
Yukich, Joshua
Chacky, Frank
Lazaro, Samwel
Dismas, Charles
Aroun, Sijenun
Ibrahim, Raya
Khamis, Mwinyi
Gitanya, Mponeja P.
Mwingizi, Deodatus
Metcalfe, Hannah
Bantanuka, Willhard
Deku, Sena
Dadi, David
Serbantez, Naomi
Loll, Dana
Koenker, Hannah
Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches
title Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches
title_full Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches
title_fullStr Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches
title_full_unstemmed Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches
title_short Estimation of bed net coverage indicators in Tanzania using mobile phone surveys: a comparison of sampling approaches
title_sort estimation of bed net coverage indicators in tanzania using mobile phone surveys: a comparison of sampling approaches
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9735037/
https://www.ncbi.nlm.nih.gov/pubmed/36496423
http://dx.doi.org/10.1186/s12936-022-04408-y
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