ArticlePublic health reports (Washington, D.C. : 1974)
Lessons Learned From Applying a Monitoring and Evaluation Framework to Economic, Social, and Other Health Impacts of the COVID-19 Pandemic.
Article in Public health reports (Washington, D.C. : 1974). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
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Abstract
Individual and community-level COVID-19 mitigation policies can have effects beyond direct COVID-19 health outcomes, including social, behavioral, and economic outcomes. These social, behavioral, and economic outcomes can extend beyond the pandemic period and have disparate effects on populations. Public Health-Seattle & King County (PHSKC) built on the Centers for Disease Control and Prevention's community mitigation strategy framework to create a local project tracking near-real-time data to understand factors affected by mitigation approaches, inform decision-making, and monitor and evaluate community-level disparities during the pandemic. This case study describes the framework and lessons learned from PHSKC's collation, use, and dissemination of local data from 20 data sources to guide community and public health decision-making. Social, behavioral, economic, and health indicators were regularly updated and disseminated through interactive dashboards and products that examined data in the context of applicable policies. Data disaggregated by demographic characteristics and geography highlighted inequities, but not all datasets contained the same details; local surveys or qualitative data were used to fill gaps. Project outcomes included informing city and county emergency response planning related to implementation of financial and food assistance programs. Key lessons learned included the need to (1) build on existing processes and use automated processes and (2) partner with other sectors to use nontraditional public health data for active dissemination and data disaggregation and for real-time data contextualized by policy changes. This project provided programs and communities with timely, reliable data to understand where to invest recovery funding. A similar framework could position other health departments to examine social and economic effects during future public health emergencies.
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