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Using Automation, Prioritization, and Collaboration to Manage a COVID-19 Case Surge in Maricopa County, Arizona, 2020.
Scott, Sarah E; Mrukowicz, Christina; Collins, Jennifer; Jehn, Megan; Charifson, Mia; Hobbs, Katherine C; Zabel, Karen; Chronister, Sara; Howard, Brandon J; White, Jessica R.
  • Scott SE; Office of Epidemiology and Data Services, Maricopa County Department of Public Health, Phoenix, AZ, USA.
  • Mrukowicz C; Office of Epidemiology and Data Services, Maricopa County Department of Public Health, Phoenix, AZ, USA.
  • Collins J; Office of Epidemiology and Data Services, Maricopa County Department of Public Health, Phoenix, AZ, USA.
  • Jehn M; School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA.
  • Charifson M; School of Human Evolution and Social Change, Arizona State University, Tempe, AZ, USA.
  • Hobbs KC; Office of Epidemiology and Data Services, Maricopa County Department of Public Health, Phoenix, AZ, USA.
  • Zabel K; Office of Epidemiology and Data Services, Maricopa County Department of Public Health, Phoenix, AZ, USA.
  • Chronister S; Office of Epidemiology and Data Services, Maricopa County Department of Public Health, Phoenix, AZ, USA.
  • Howard BJ; Office of Epidemiology and Data Services, Maricopa County Department of Public Health, Phoenix, AZ, USA.
  • White JR; Office of Epidemiology and Data Services, Maricopa County Department of Public Health, Phoenix, AZ, USA.
Public Health Rep ; 137(2_suppl): 29S-34S, 2022.
Article in English | MEDLINE | ID: covidwho-1916703
ABSTRACT
During summer 2020, the Maricopa County Department of Public Health (MCDPH) responded to a surge in COVID-19 cases. We used internet-based platforms to automate case notifications, prioritized investigation of cases more likely to have onward transmission or severe COVID-19 based on available preinvestigation information, and partnered with Arizona State University (ASU) to scale investigation capacity. We assessed the speed of automated case notifications and accuracy of our investigation prioritization criteria. Timeliness of case notification-the median time between receipt of a case report at MCDPH and first case contact-improved from 11 days to <1 day after implementation of automated case notification. We calculated the sensitivity and positive predictive value (PPV) of the investigation prioritization system by applying our high-risk prioritization criteria separately to data available pre- and postinvestigation to determine whether a case met these criteria preinvestigation, postinvestigation, or both. We calculated the sensitivity as the percentage of cases classified postinvestigation as high risk that had also been classified as high risk preinvestigation. We calculated PPV as the percentage of all cases deemed high risk preinvestigation that remained so postinvestigation. During June 30 to July 31, 2020, a total of 55 056 COVID-19 cases with an associated telephone number (94% of 58 570 total cases) were reported. Preinvestigation, 8799 (16%) cases met high-risk criteria. Postinvestigation, 17 037 (31%) cases met high-risk criteria. Sensitivity was 52% and PPV was 98%. Automating case notifications, prioritizing investigations, and collaborating with ASU improved the timeliness of case contact, focused public health resources toward high-priority cases, and increased investigation capacity. Establishing partnerships between health departments and academia might be a helpful strategy for future surge capacity planning.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Case report / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: Public Health Rep Year: 2022 Document Type: Article Affiliation country: 00333549221100798

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Full text: Available Collection: International databases Database: MEDLINE Main subject: COVID-19 Type of study: Case report / Observational study / Prognostic study Limits: Humans Country/Region as subject: North America Language: English Journal: Public Health Rep Year: 2022 Document Type: Article Affiliation country: 00333549221100798