Parada Vanessa, Fast Larissa, Briody Carolyn, Wille Christina, Coninx Rudi
Helpcode, Genoa, Italy.
HCRI, University of Manchester, Manchester, UK.
Confl Health. 2023 Jan 30;17(1):3. doi: 10.1186/s13031-023-00498-w.
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.
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.
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.
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.
对医疗保健的攻击是一个日益引起国际关注的领域。公开可用的数据对于记录攻击事件很重要，而且往往是唯一易于获取的数据源。尽管数据集及其收集过程可能导致不同的数字，但关于对健康的攻击及其影响的数据收集过程却很少受到关注。比较两个使用公开可用数据编制的单独数据集发现重叠极少。本文旨在解释缺乏重叠的原因，以更好地理解差距及其影响。
我们比较了世界卫生组织（WHO）公开报告的对医疗保健攻击的数据集和不安全洞察组织的“数字中的安全数据库”（SiND）的数据收集过程。我们比较每个单独事件以编制可比数据集并识别独特和匹配的事件，以确定它们之间的重叠。我们报告此比较的描述性统计数据。
我们确定了一个2017年的287个事件的共同数据集，其中只有33个出现在两个数据集中，重叠率仅为12.9%（n = 254）。影响人员和设施的事件在两个数据集中出现得最为频繁，31个国家中有22个国家的数据集之间没有任何重叠。
我们得出结论，极小的重叠表明对医疗保健攻击的报告严重不足，此外，数据集的定义和参数会影响数据收集。来源差异似乎最能解释这些差异，对收集过程进行更仔细的比较揭示了依赖隐藏策展过程的自动和手动数据收集的弱点。要生成从公共来源编制的更准确的数据集，需要系统地将定义转化为有效的在线搜索机制，以更好地捕捉所有事件，并增加语言和本地来源的多样性，以更好地捕捉不同地区的事件。
