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关于增加联邦获取州和地方国家综合征监测计划数据的意见:一项针对州和地方流行病学家的名义群体技术研究。

Views on increased federal access to state and local National Syndromic Surveillance Program data: a nominal group technique study with state and local epidemiologists.

机构信息

Program in Health Law and Policy, Texas A&M University School of Public Health, 212 Adriance Lab Rd, TX, 77843, College Station, USA.

Texas A&M University School of Law, 1515 Commerce St, 76102, Fort Worth, TX, USA.

出版信息

BMC Public Health. 2023 Mar 6;23(1):431. doi: 10.1186/s12889-023-15161-5.

DOI:10.1186/s12889-023-15161-5
PMID:36879220
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9987142/
Abstract

BACKGROUND

US public health authorities use syndromic surveillance to monitor and detect public health threats, conditions, and trends in near real-time. Nearly all US jurisdictions that conduct syndromic surveillance send their data to the National Syndromic Surveillance Program (NSSP), operated by the US. Centers for Disease Control and Prevention. However, current data sharing agreements limit federal access to state and local NSSP data to only multi-state regional aggregations. This limitation was a significant challenge for the national response to COVID-19. This study seeks to understand state and local epidemiologists' views on increased federal access to state NSSP data and identify policy opportunities for public health data modernization.

METHODS

In September 2021, we used a virtual, modified nominal group technique with twenty regionally diverse epidemiologists in leadership positions and three individuals representing national public health organizations. Participants individually generated ideas on benefits, concerns, and policy opportunities relating to increased federal access to state and local NSSP data. In small groups, participants clarified and grouped the ideas into broader themes with the assistance of the research team. An web-based survey was used to evaluate and rank the themes using five-point Likert importance questions, top-3 ranking questions, and open-ended response questions.

RESULTS

Participants identified five benefit themes for increased federal access to jurisdictional NSSP data, with the most important being improved cross-jurisdiction collaboration (mean Likert = 4.53) and surveillance practice (4.07). Participants identified nine concern themes, with the most important concerns being federal actors using jurisdictional data without notice (4.60) and misinterpretation of data (4.53). Participants identified eleven policy opportunities, with the most important being involving state and local partners in analysis (4.93) and developing communication protocols (4.53).

CONCLUSION

These findings identify barriers and opportunities to federal-state-local collaboration critical to current data modernization efforts. Syndromic surveillance considerations warrant data-sharing caution. However, identified policy opportunities share congruence with existing legal agreements, suggesting that syndromic partners are closer to agreement than they might realize. Moreover, several policy opportunities (i.e., including state and local partners in data analysis and developing communication protocols) received consensus support and provide a promising path forward.

摘要

背景

美国公共卫生当局利用综合征监测系统实时监测和发现公共卫生威胁、状况和趋势。几乎所有进行综合征监测的美国司法管辖区都将其数据发送到美国疾病控制与预防中心运营的国家综合征监测计划 (NSSP)。然而,目前的数据共享协议仅将联邦机构获取州和地方 NSSP 数据的权限限制在多州区域汇总。这一限制是国家对 COVID-19 应对的重大挑战。本研究旨在了解州和地方流行病学家对增加联邦机构获取州 NSSP 数据的看法,并确定公共卫生数据现代化的政策机会。

方法

2021 年 9 月,我们使用虚拟的、经修改的名义群体技术,在 20 名具有领导地位的区域多样化流行病学家和 3 名代表国家公共卫生组织的人员中进行了这项研究。参与者分别就增加联邦机构获取州和地方 NSSP 数据的益处、关注和政策机会提出了意见。在研究小组的协助下,小组成员在澄清和将想法分为更广泛的主题方面进行了合作。我们使用在线调查,通过五分制李克特重要性问题、前三排名问题和开放式回答问题对主题进行评估和排名。

结果

参与者确定了五个增加联邦机构获取辖区 NSSP 数据的益处主题,最重要的是提高跨辖区协作(平均李克特=4.53)和监测实践(4.07)。参与者确定了九个关注主题,最重要的是联邦行为者在未经通知的情况下使用辖区数据(4.60)和数据误读(4.53)。参与者确定了十一个政策机会,最重要的是让州和地方合作伙伴参与分析(4.93)和制定沟通协议(4.53)。

结论

这些发现确定了对当前数据现代化努力至关重要的联邦-州-地方合作的障碍和机会。综合征监测的考虑因素需要谨慎进行数据共享。然而,确定的政策机会与现有法律协议具有一致性,这表明综合征合作伙伴比他们意识到的更接近协议。此外,一些政策机会(即让州和地方合作伙伴参与数据分析和制定沟通协议)得到了共识支持,并为前进提供了有希望的途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8784/9987142/1777cdb1422e/12889_2023_15161_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8784/9987142/1777cdb1422e/12889_2023_15161_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8784/9987142/1777cdb1422e/12889_2023_15161_Fig1_HTML.jpg

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