Using Electronic Health Records to Enhance Lyme Disease Surveillance: Protocol for the SubLyme Network

Document Type

Article

Publication Date

7-8-2026

Institution/Department

Lyme and Vector-Borne Disease Laboratory

Journal Title

JMIR research protocols

MeSH Headings

Lyme Disease (epidemiology, diagnosis); Electronic Health Records (statistics & numerical data); Humans; United States (epidemiology); Population Surveillance (methods); Centers for Disease Control and Prevention, U.S. (organization & administration); Incidence

Abstract

BACKGROUND: Lyme disease is the most common vector-borne illness in the United States. The limitations of traditional surveillance strategies for Lyme disease affect the ability to reliably track its burden and evaluate interventions. The US Centers for Disease Control and Prevention (CDC) established the Surveillance Based Lyme Disease Network (SubLyme) in September 2023 to strengthen Lyme disease surveillance and research using electronic health record (EHR) data. OBJECTIVE: SubLyme has three primary objectives: (1) to establish and evaluate criteria for identifying Lyme disease cases in EHR data (ie, create computable phenotypes [CPs]) that can be scaled across diverse health systems, (2) to estimate Lyme disease incidence, and (3) to describe Lyme disease incidence by key demographics. Secondary objectives are to develop CPs that distinguish between acute and disseminated Lyme disease, identify clinical manifestations, and support future research efforts. This paper describes SubLyme, its structure, and its methods. METHODS: SubLyme includes 5 health systems in 3 US regions with a high risk of Lyme disease: Geisinger, in Pennsylvania; Marshfield Clinic Health System, in Wisconsin; and Mass General Brigham, Tufts Medical Center, and MaineHealth in New England. The network is administered by a coordinating center (Westat) and the US CDC. SubLyme is evaluating the validity of EHR-based CP definitions for Lyme disease. CP performance is assessed by measuring sensitivity, specificity, positive predictive value, and negative predictive value against manually abstracted medical charts. Each site identified a cohort of patients with any Lyme disease element in their EHR (Lyme disease diagnosis code, Lyme disease laboratory test order, and Lyme-appropriate antibiotic order) during 2022 to 2023 and selected 500 charts for manual review as the gold standard against which CP performance was evaluated. SubLyme will use the Lyme disease CPs to generate incidence rates for Lyme disease overall and for various subgroups. RESULTS: SubLyme identified 332,256 patients with at least 1 Lyme disease element in their record from more than 4.6 million patients. Of these patients, 55.6% (n=184,734) were female, 87.9% (n=292,053) were White, and 90.8% (n=301,688) were non-Hispanic. More than half of the patients only had a Lyme-appropriate medication order (n=177,425, 53.4%) and 35.8% (n=118,948) only had a Lyme disease test order. The most common combination was a medication order with a laboratory test order (n=22,926, 6.9%), followed by a combination of a diagnosis, test, and medication order (n=5316, 1.6%). CONCLUSIONS: SubLyme is well positioned to advance Lyme disease surveillance using EHR data across multiple health systems. The exploration of new surveillance methods in Lyme disease is critical as disease frequency increases and the geography expands. An EHR-based approach to surveillance has the potential to overcome challenges of current surveillance strategies and to accelerate Lyme disease research. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/94921.

First Page

e94921

Link to Full Text

Share

COinS