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The effect of sampling health facilities on estimates of effective coverage: a simulation study

BACKGROUND: Most existing facility assessments collect data on a sample of health facilities. Sampling of health facilities may introduce bias into estimates of effective coverage generated by ecologically linking individuals to health providers based on geographic proximity or administrative catchm...

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Autores principales: Carter, Emily D., Maiga, Abdoulaye, Do, Mai, Sika, Glebelho Lazare, Mosso, Rosine, Dosso, Abdul, Munos, Melinda K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758803/
https://www.ncbi.nlm.nih.gov/pubmed/36528582
http://dx.doi.org/10.1186/s12942-022-00307-2
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author Carter, Emily D.
Maiga, Abdoulaye
Do, Mai
Sika, Glebelho Lazare
Mosso, Rosine
Dosso, Abdul
Munos, Melinda K.
author_facet Carter, Emily D.
Maiga, Abdoulaye
Do, Mai
Sika, Glebelho Lazare
Mosso, Rosine
Dosso, Abdul
Munos, Melinda K.
author_sort Carter, Emily D.
collection PubMed
description BACKGROUND: Most existing facility assessments collect data on a sample of health facilities. Sampling of health facilities may introduce bias into estimates of effective coverage generated by ecologically linking individuals to health providers based on geographic proximity or administrative catchment. METHODS: We assessed the bias introduced to effective coverage estimates produced through two ecological linking approaches (administrative unit and Euclidean distance) applied to a sample of health facilities. Our analysis linked MICS household survey data on care-seeking for child illness and childbirth care with data on service quality collected from a census of health facilities in the Savanes region of Cote d’Ivoire. To assess the bias introduced by sampling, we drew 20 random samples of three different sample sizes from our census of health facilities. We calculated effective coverage of sick child and childbirth care using both ecological linking methods applied to each sampled facility data set. We compared the sampled effective coverage estimates to ecologically linked census-based estimates and estimates based on true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores. RESULTS: Sampling of health facilities did not significantly bias effective coverage compared to either the ecologically linked estimates derived from a census of facilities or true effective coverage estimates using the original data or simulated random quality sensitivity analysis. However, a few estimates based on sampling in a setting where individuals preferentially sought care from higher-quality providers fell outside of the estimate bounds of true effective coverage. Those cases predominantly occurred using smaller sample sizes and the Euclidean distance linking method. None of the sample-based estimates fell outside the bounds of the ecologically linked census-derived estimates. CONCLUSIONS: Our analyses suggest that current health facility sampling approaches do not significantly bias estimates of effective coverage produced through ecological linking. Choice of ecological linking methods is a greater source of bias from true effective coverage estimates, although facility sampling can exacerbate this bias in certain scenarios. Careful selection of ecological linking methods is essential to minimize the potential effect of both ecological linking and sampling error.
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spelling pubmed-97588032022-12-18 The effect of sampling health facilities on estimates of effective coverage: a simulation study Carter, Emily D. Maiga, Abdoulaye Do, Mai Sika, Glebelho Lazare Mosso, Rosine Dosso, Abdul Munos, Melinda K. Int J Health Geogr Research BACKGROUND: Most existing facility assessments collect data on a sample of health facilities. Sampling of health facilities may introduce bias into estimates of effective coverage generated by ecologically linking individuals to health providers based on geographic proximity or administrative catchment. METHODS: We assessed the bias introduced to effective coverage estimates produced through two ecological linking approaches (administrative unit and Euclidean distance) applied to a sample of health facilities. Our analysis linked MICS household survey data on care-seeking for child illness and childbirth care with data on service quality collected from a census of health facilities in the Savanes region of Cote d’Ivoire. To assess the bias introduced by sampling, we drew 20 random samples of three different sample sizes from our census of health facilities. We calculated effective coverage of sick child and childbirth care using both ecological linking methods applied to each sampled facility data set. We compared the sampled effective coverage estimates to ecologically linked census-based estimates and estimates based on true source of care. We performed sensitivity analyses with simulated preferential care-seeking from higher-quality providers and randomly generated provider quality scores. RESULTS: Sampling of health facilities did not significantly bias effective coverage compared to either the ecologically linked estimates derived from a census of facilities or true effective coverage estimates using the original data or simulated random quality sensitivity analysis. However, a few estimates based on sampling in a setting where individuals preferentially sought care from higher-quality providers fell outside of the estimate bounds of true effective coverage. Those cases predominantly occurred using smaller sample sizes and the Euclidean distance linking method. None of the sample-based estimates fell outside the bounds of the ecologically linked census-derived estimates. CONCLUSIONS: Our analyses suggest that current health facility sampling approaches do not significantly bias estimates of effective coverage produced through ecological linking. Choice of ecological linking methods is a greater source of bias from true effective coverage estimates, although facility sampling can exacerbate this bias in certain scenarios. Careful selection of ecological linking methods is essential to minimize the potential effect of both ecological linking and sampling error. BioMed Central 2022-12-17 /pmc/articles/PMC9758803/ /pubmed/36528582 http://dx.doi.org/10.1186/s12942-022-00307-2 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
Carter, Emily D.
Maiga, Abdoulaye
Do, Mai
Sika, Glebelho Lazare
Mosso, Rosine
Dosso, Abdul
Munos, Melinda K.
The effect of sampling health facilities on estimates of effective coverage: a simulation study
title The effect of sampling health facilities on estimates of effective coverage: a simulation study
title_full The effect of sampling health facilities on estimates of effective coverage: a simulation study
title_fullStr The effect of sampling health facilities on estimates of effective coverage: a simulation study
title_full_unstemmed The effect of sampling health facilities on estimates of effective coverage: a simulation study
title_short The effect of sampling health facilities on estimates of effective coverage: a simulation study
title_sort effect of sampling health facilities on estimates of effective coverage: a simulation study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9758803/
https://www.ncbi.nlm.nih.gov/pubmed/36528582
http://dx.doi.org/10.1186/s12942-022-00307-2
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