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Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies
BACKGROUND: Healthcare-associated infections (HAIs) represent a major Public Health issue. Hospital-based prevalence studies are a common tool of HAI surveillance, but data quality problems and non-representativeness can undermine their reliability. METHODS: This study proposes three algorithms that...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8088017/ https://www.ncbi.nlm.nih.gov/pubmed/33931025 http://dx.doi.org/10.1186/s12874-021-01277-y |
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author | D’Ambrosio, A. Garlasco, J. Quattrocolo, F. Vicentini, C. Zotti, C. M. |
author_facet | D’Ambrosio, A. Garlasco, J. Quattrocolo, F. Vicentini, C. Zotti, C. M. |
author_sort | D’Ambrosio, A. |
collection | PubMed |
description | BACKGROUND: Healthcare-associated infections (HAIs) represent a major Public Health issue. Hospital-based prevalence studies are a common tool of HAI surveillance, but data quality problems and non-representativeness can undermine their reliability. METHODS: This study proposes three algorithms that, given a convenience sample and variables relevant for the outcome of the study, select a subsample with specific distributional characteristics, boosting either representativeness (Probability and Distance procedures) or risk factors’ balance (Uniformity procedure). A “Quality Score” (QS) was also developed to grade sampled units according to data completeness and reliability. The methodologies were evaluated through bootstrapping on a convenience sample of 135 hospitals collected during the 2016 Italian Point Prevalence Survey (PPS) on HAIs. RESULTS: The QS highlighted wide variations in data quality among hospitals (median QS 52.9 points, range 7.98–628, lower meaning better quality), with most problems ascribable to ward and hospital-related data reporting. Both Distance and Probability procedures produced subsamples with lower distributional bias (Log-likelihood score increased from 7.3 to 29 points). The Uniformity procedure increased the homogeneity of the sample characteristics (e.g., − 58.4% in geographical variability). The procedures selected hospitals with higher data quality, especially the Probability procedure (lower QS in 100% of bootstrap simulations). The Distance procedure produced lower HAI prevalence estimates (6.98% compared to 7.44% in the convenience sample), more in line with the European median. CONCLUSIONS: The QS and the subsampling procedures proposed in this study could represent effective tools to improve the quality of prevalence studies, decreasing the biases that can arise due to non-probabilistic sample collection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01277-y. |
format | Online Article Text |
id | pubmed-8088017 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-80880172021-05-03 Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies D’Ambrosio, A. Garlasco, J. Quattrocolo, F. Vicentini, C. Zotti, C. M. BMC Med Res Methodol Technical Advance BACKGROUND: Healthcare-associated infections (HAIs) represent a major Public Health issue. Hospital-based prevalence studies are a common tool of HAI surveillance, but data quality problems and non-representativeness can undermine their reliability. METHODS: This study proposes three algorithms that, given a convenience sample and variables relevant for the outcome of the study, select a subsample with specific distributional characteristics, boosting either representativeness (Probability and Distance procedures) or risk factors’ balance (Uniformity procedure). A “Quality Score” (QS) was also developed to grade sampled units according to data completeness and reliability. The methodologies were evaluated through bootstrapping on a convenience sample of 135 hospitals collected during the 2016 Italian Point Prevalence Survey (PPS) on HAIs. RESULTS: The QS highlighted wide variations in data quality among hospitals (median QS 52.9 points, range 7.98–628, lower meaning better quality), with most problems ascribable to ward and hospital-related data reporting. Both Distance and Probability procedures produced subsamples with lower distributional bias (Log-likelihood score increased from 7.3 to 29 points). The Uniformity procedure increased the homogeneity of the sample characteristics (e.g., − 58.4% in geographical variability). The procedures selected hospitals with higher data quality, especially the Probability procedure (lower QS in 100% of bootstrap simulations). The Distance procedure produced lower HAI prevalence estimates (6.98% compared to 7.44% in the convenience sample), more in line with the European median. CONCLUSIONS: The QS and the subsampling procedures proposed in this study could represent effective tools to improve the quality of prevalence studies, decreasing the biases that can arise due to non-probabilistic sample collection. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-021-01277-y. BioMed Central 2021-04-30 /pmc/articles/PMC8088017/ /pubmed/33931025 http://dx.doi.org/10.1186/s12874-021-01277-y Text en © The Author(s) 2021 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 | Technical Advance D’Ambrosio, A. Garlasco, J. Quattrocolo, F. Vicentini, C. Zotti, C. M. Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies |
title | Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies |
title_full | Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies |
title_fullStr | Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies |
title_full_unstemmed | Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies |
title_short | Data quality assessment and subsampling strategies to correct distributional bias in prevalence studies |
title_sort | data quality assessment and subsampling strategies to correct distributional bias in prevalence studies |
topic | Technical Advance |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8088017/ https://www.ncbi.nlm.nih.gov/pubmed/33931025 http://dx.doi.org/10.1186/s12874-021-01277-y |
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