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

IF 3.8 Q2 INFECTIOUS DISEASES
Hanna N Oltean, Beth Lipton, Allison Black, Kevin Snekvik, Katie Haman, Minden Buswell, Anna E Baines, Peter M Rabinowitz, Shannon L Russell, Sean Shadomy, Ria R Ghai, Steven Rekant, Scott Lindquist, Janet G Baseman
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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.

开发以实时病原体监测和应用基因组流行病学为重点的单一卫生数据整合框架。
背景:“同一个健康”方针旨在平衡和优化人类、动物和生态系统的健康,认识到共享的健康结果是相互依存的。对疾病监测、控制和预防采取“同一个健康”方针,需要用于协调、收集、整合和分析跨部门数据的基础设施,包括人类、动物和环境监测数据以及病原体基因组数据。然而,与单个组织或部门内部面临的数据互操作性问题不同,“一个健康”部门之间的数据协调和整合需要合作伙伴的参与,以便在应对层面建立共同的目标和能力。成功的例子很少;因此,我们试图为当地的“同一健康”从业者制定一个框架,以支持这些努力。方法:我们进行了系统的科学和灰色文献综述,为One Health数据集成框架的开发提供信息。在半结构化访谈中,我们与17位One Health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:最终研究共纳入了57份记录,代表了卫生系统、“一个健康”或数据整合的13个预定义框架。这些框架(包括文章和专家反馈)被整合到One Health数据集成的新框架中。在文献中确定并概述了基因组数据整合的两种情况。结论:目前存在“一个健康”数据整合框架和单独的一般信息学流程框架;然而,将它们整合和应用于实时疾病监测引起了独特的考虑。这里开发的框架考虑了有限资源设置的共同挑战,包括在计划期间缺乏信息支持,以及需要超越范围和计划到系统开发、生产和联合分析。几个重要的考虑因素将“同一个健康”框架与更广义的信息学框架区分开来;这些包括复杂的合作伙伴识别、参与和共同开发系统范围的需求、复杂的数据治理,以及跨部门成功联合数据分析、报告和解释的需求。该框架将支持在响应层面实施数据整合,为即将发生的“同一个健康”事件提供早期预警,促进确定新的假设和见解,并允许综合的“同一个健康”解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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