Syndromic Surveillance for COVID-19, Massachusetts, February 2020-November 2022: The Impact of Fever and Severity on Algorithm Performance

Public Health Rep. 2023 Sep-Oct;138(5):756-762. doi: 10.1177/00333549231186574. Epub 2023 Jul 21.

Abstract

Objectives: Syndromic surveillance can help identify the onset, location, affected populations, and trends in infectious diseases quickly and efficiently. We developed an electronic medical record-based surveillance algorithm for COVID-19-like illness (CLI) and assessed its performance in 5 Massachusetts medical practice groups compared with statewide counts of confirmed cases.

Materials and methods: Using data from February 2020 through November 2022, the CLI algorithm was implemented in sites that provide ambulatory and inpatient care for about 25% of the state. The initial algorithm for CLI was modeled on influenza-like illness: an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) diagnosis code for COVID-19 and an ICD-10-CM diagnosis code suggesting severe lower respiratory tract infection or ≥1 ICD-10-CM diagnosis code for upper or lower respiratory tract infection plus fever. We generated weekly counts of CLI cases and patients with ≥1 clinical encounter and visually compared trends with those of statewide laboratory-confirmed cases.

Results: The initial algorithm tracked well with the spring 2020 wave of COVID-19, but the components that required fever did not clearly detect the November 2020-January 2021 surge and identified <1% of weekly encounters as CLI. We revised the algorithm by adding more mild symptoms and removing the fever requirement; this revision improved alignment with statewide confirmed cases through spring 2022 and increased the proportion of encounters identified as CLI to about 2% to 6% weekly. Alignment between CLI trends and confirmed COVID-19 case counts diverged again in fall 2022, likely because of decreased COVID-19 testing and increases in other respiratory viruses.

Practice implications: Our work highlights the importance of using a broad definition for COVID-19 syndromic surveillance and the need for surveillance systems that are flexible and adaptable to changing trends and patterns in disease or care.

Keywords: COVID-19; influenza-like illness; syndromic surveillance.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • COVID-19 Testing
  • COVID-19* / epidemiology
  • Humans
  • Massachusetts / epidemiology
  • Respiratory Tract Infections*
  • Sentinel Surveillance