Centers for Disease Control and Prevention, Atlanta, GA, USA.
Deloitte Consulting LLP, San Francisco, CA, USA.
Public Health Rep. 2023 Nov-Dec;138(6):878-884. doi: 10.1177/00333549231190050. Epub 2023 Sep 7.
During the COVID-19 pandemic, an urgent need existed for near-real-time data collection to better understand how individual beliefs and behaviors, state and local policies, and organizational practices influenced health outcomes. We describe the processes, methods, and lessons learned during the development and pilot testing of an innovative rapid data collection process we developed to inform decision-making during the COVID-19 public health emergency. We used a fully integrated mixed-methods approach to develop a structured process for triangulating quantitative and qualitative data from traditional (cross-sectional surveys, focus groups) and nontraditional (social media listening) sources. Respondents included students, parents, teachers, and key school personnel (eg, nurses, administrators, mental health providers). During the pilot phase (February-June 2021), data from 12 cross-sectional and sector-based surveys (n = 20 302 participants), 28 crowdsourced surveys (n = 26 820 participants), 10 focus groups (n = 64 participants), and 11 social media platforms (n = 432 754 503 responses) were triangulated with other data to support COVID-19 mitigation in schools. We disseminated findings through internal dashboards, triangulation reports, and policy briefs. This pilot demonstrated that triangulating traditional and nontraditional data sources can provide rapid data about barriers and facilitators to mitigation implementation during an evolving public health emergency. Such a rapid feedback and continuous improvement model can be tailored to strengthen response efforts. This approach emphasizes the value of nimble data modernization efforts to respond in real time to public health emergencies.
在 COVID-19 大流行期间,迫切需要进行近乎实时的数据收集,以便更好地了解个人信念和行为、州和地方政策以及组织实践如何影响健康结果。我们描述了在开发和试点测试期间的过程、方法和经验教训,我们开发了一种创新的快速数据收集流程,以在 COVID-19 公共卫生紧急情况下为决策提供信息。我们使用完全集成的混合方法方法来开发一种结构化的过程,用于对来自传统(横断面调查、焦点小组)和非传统(社交媒体监听)来源的定量和定性数据进行三角剖分。受访者包括学生、家长、教师和关键学校人员(例如护士、管理员、心理健康提供者)。在试点阶段(2021 年 2 月至 6 月),来自 12 项横断面和基于部门的调查(n=20302 名参与者)、28 项众包调查(n=26820 名参与者)、10 个焦点小组(n=64 名参与者)和 11 个社交媒体平台(n=432754503 条回复)的数据与其他数据进行了三角剖分,以支持学校的 COVID-19 缓解措施。我们通过内部仪表板、三角剖分报告和政策简报传播研究结果。该试点表明,对传统和非传统数据源进行三角剖分可以提供有关在不断演变的公共卫生紧急情况下缓解措施实施的障碍和促进因素的快速数据。这种快速反馈和持续改进模型可以针对加强应对工作进行定制。这种方法强调了灵活的数据现代化工作的价值,以实时应对公共卫生紧急情况。