JMIR Publications

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 burden and evaluate interventions. The US Centers for Disease Control and Prevention (CDC) established the Surveillance Based Lyme Disease Network (SubLyme) to strengthen Lyme disease surveillance and research using electronic health record (EHR) data.

Objective:

The network has three primary objectives: 1) to establish and evaluate criteria for identifying Lyme disease cases in EHR data (i.e., create computable phenotypes [CPs]) that can be scaled across diverse health systems); 2) to use the CPs to estimate Lyme disease incidence; and 3) to describe Lyme disease incidence by key demographics. Secondary objectives are to develop alternative CPs that can distinguish between acute and disseminated Lyme disease, identify clinical manifestations for estimates of disease severity, and support future research efforts. This paper describes SubLyme, its structure, and the methods that will be used to achieve these objectives.

Methods:

SubLyme includes five health systems in three U.S. regions with a high risk of Lyme disease: Geisinger (Pennsylvania); Marshfield Clinic Health System (Wisconsin); and three health systems in New England—Mass General Brigham (MGB), Tufts Medical Center, and MaineHealth, administered by a coordinating center (Westat) and CDC. The network 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 elements in their EHR (Lyme disease diagnosis code, Lyme disease laboratory test order, Lyme-appropriate antibiotic order) during 2022–2023 and selected 500 charts for manual review as the gold standard against which to evaluate CP performance. 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 one Lyme disease element in their record from more than 4.6 million patients. This group of patients was 55.6% female, 87.9% White, and 90.8% non-Hispanic. More than half of patients only had a Lyme-appropriate medication order (53.4%) and 35.8% only had a Lyme disease test order. The most common combination was a medication order with a laboratory test order (6.9%), followed by a combination of a diagnosis, test, and medication order (1.6%).

Conclusions:

SubLyme is well-positioned to advance Lyme disease surveillance through the use of 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 existing challenges of current surveillance strategies to accelerate Lyme disease research. Clinical Trial: N/A