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

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.