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开发一个专注于实时病原体监测和应用基因组流行病学的一体化健康数据整合框架。

Developing a one health data integration framework focused on real-time pathogen surveillance and applied genomic epidemiology.

作者信息

Oltean Hanna N, Lipton Beth, Black Allison, Snekvik Kevin, Haman Katie, Buswell Minden, Baines Anna E, Rabinowitz Peter M, Russell Shannon L, Shadomy Sean, Ghai Ria R, Rekant Steven, Lindquist Scott, Baseman Janet G

机构信息

Washington State Department of Health, 1610 NE 150th St, Shoreline, WA, 98155, USA.

University of Washington, 1410 NE Campus Parkway, 98195, Seattle, Washington, USA.

出版信息

One Health Outlook. 2025 Feb 20;7(1):9. doi: 10.1186/s42522-024-00133-5.

Abstract

BACKGROUND

The One Health approach aims to balance and optimize the health of humans, animals, and ecosystems, recognizing that shared health outcomes are interdependent. A One Health approach to disease surveillance, control, and prevention requires infrastructure for coordinating, collecting, integrating, and analyzing data across sectors, incorporating human, animal, and environmental surveillance data, as well as pathogen genomic data. However, unlike data interoperability problems faced within a single organization or sector, data coordination and integration across One Health sectors requires engagement among partners to develop shared goals and capacity at the response level. Successful examples are rare; as such, we sought to develop a framework for local One Health practitioners to utilize in support of such efforts.

METHODS

We conducted a systematic scientific and gray literature review to inform development of a One Health data integration framework. We discussed a draft framework with 17 One Health and informatics experts during semi-structured interviews. Approaches to genomic data integration were identified.

RESULTS

In total, 57 records were included in the final study, representing 13 pre-defined frameworks for health systems, One Health, or data integration. These frameworks, included articles, and expert feedback were incorporated into a novel framework for One Health data integration. Two scenarios for genomic data integration were identified in the literature and outlined.

CONCLUSIONS

Frameworks currently exist for One Health data integration and separately for general informatics processes; however, their integration and application to real-time disease surveillance raises unique considerations. The framework developed herein considers common challenges of limited resource settings, including lack of informatics support during planning, and the need to move beyond scoping and planning to system development, production, and joint analyses. Several important considerations separate this One Health framework from more generalized informatics frameworks; these include complex partner identification, requirements for engagement and co-development of system scope, complex data governance, and a requirement for joint data analysis, reporting, and interpretation across sectors for success. This framework will support operationalization of data integration at the response level, providing early warning for impending One Health events, promoting identification of novel hypotheses and insights, and allowing for integrated One Health solutions.

摘要

背景

“同一健康”方法旨在平衡并优化人类、动物和生态系统的健康,认识到共同的健康结果是相互依存的。采用“同一健康”方法进行疾病监测、控制和预防需要具备跨部门协调、收集、整合和分析数据的基础设施,纳入人类、动物和环境监测数据以及病原体基因组数据。然而,与单个组织或部门面临的数据互操作性问题不同,跨“同一健康”部门的数据协调和整合需要合作伙伴之间的参与,以在应对层面制定共同目标和能力。成功的例子很少;因此,我们试图为当地的“同一健康”从业者开发一个框架,以支持此类工作。

方法

我们进行了系统的科学文献和灰色文献综述,为“同一健康”数据整合框架的开发提供信息。在半结构化访谈期间,我们与17位“同一健康”和信息学专家讨论了框架草案。确定了基因组数据整合的方法。

结果

最终研究共纳入57条记录,代表了13个针对卫生系统、“同一健康”或数据整合的预定义框架。这些框架、纳入的文章以及专家反馈被纳入一个新的“同一健康”数据整合框架。在文献中确定并概述了两种基因组数据整合方案。

结论

目前存在针对“同一健康”数据整合的框架,也分别存在针对一般信息学过程的框架;然而,将它们整合并应用于实时疾病监测会引发独特的考虑因素。本文开发的框架考虑了资源有限环境的常见挑战,包括规划期间缺乏信息学支持,以及需要从范围界定和规划转向系统开发、生产和联合分析。这个“同一健康”框架与更通用的信息学框架有几个重要区别;这些包括复杂的合作伙伴识别、系统范围参与和共同开发的要求、复杂的数据治理,以及跨部门成功进行联合数据分析、报告和解释的要求。该框架将支持在应对层面实现数据整合的操作化,为即将发生的“同一健康”事件提供早期预警,促进新假设和见解的识别,并实现“同一健康”的综合解决方案。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f7cc/11841253/ac43ec2a3d98/42522_2024_133_Fig1_HTML.jpg

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