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Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017

BACKGROUND: Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little att...

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Autores principales: Parada, Vanessa, Fast, Larissa, Briody, Carolyn, Wille, Christina, Coninx, Rudi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885616/
https://www.ncbi.nlm.nih.gov/pubmed/36717946
http://dx.doi.org/10.1186/s13031-023-00498-w
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author Parada, Vanessa
Fast, Larissa
Briody, Carolyn
Wille, Christina
Coninx, Rudi
author_facet Parada, Vanessa
Fast, Larissa
Briody, Carolyn
Wille, Christina
Coninx, Rudi
author_sort Parada, Vanessa
collection PubMed
description BACKGROUND: Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. METHODS: We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. RESULTS: We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. CONCLUSIONS: We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies.
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spelling pubmed-98856162023-01-31 Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017 Parada, Vanessa Fast, Larissa Briody, Carolyn Wille, Christina Coninx, Rudi Confl Health Research BACKGROUND: Attacks on health care represent an area of growing international concern. Publicly available data are important in documenting attacks, and are often the only easily accessible data source. Data collection processes about attacks on health and their implications have received little attention, despite the fact that datasets and their collection processes may result in differing numbers. Comparing two separate datasets compiled using publicly-available data revealed minimal overlap. This article aims to explain the reasons for the lack of overlap, to better understand the gaps and their implications. METHODS: We compared the data collection processes for datasets comprised of publicly-reported attacks on health care from the World Health Organization (WHO) and Insecurity Insight’s Security in Numbers Database (SiND). We compared each individual event to compile a comparable dataset and identify unique and matched events in order to determine the overlap between them. We report descriptive statistics for this comparison. RESULTS: We identified a common dataset of 287 events from 2017, of which only 33 appeared in both datasets, resulting in a mere 12.9% (n = 254) overlap. Events affecting personnel and facilities appeared most often in both, and 22 of 31 countries lacked any overlap between datasets. CONCLUSIONS: We conclude that the minimal overlap suggests significant underreporting of attacks on health care, and furthermore, that dataset definitions and parameters affect data collection. Source variation appears to best explain the discrepancies and closer comparison of the collection processes reveal weaknesses of both automated and manual data collection that rely on hidden curation processes. To generate more accurate datasets compiled from public sources requires systematic work to translate definitions into effective online search mechanisms to better capture the full range of events, and to increase the diversity of languages and local sources to better capture events across geographies. BioMed Central 2023-01-30 /pmc/articles/PMC9885616/ /pubmed/36717946 http://dx.doi.org/10.1186/s13031-023-00498-w Text en © The Author(s) 2023 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
Parada, Vanessa
Fast, Larissa
Briody, Carolyn
Wille, Christina
Coninx, Rudi
Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017
title Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017
title_full Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017
title_fullStr Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017
title_full_unstemmed Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017
title_short Underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017
title_sort underestimating attacks: comparing two sources of publicly-available data about attacks on health care in 2017
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9885616/
https://www.ncbi.nlm.nih.gov/pubmed/36717946
http://dx.doi.org/10.1186/s13031-023-00498-w
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