Potential quantitative effect of a laboratory-based approach to Lyme disease surveillance in high-incidence states.

Historically, public health surveillance for Lyme disease has required clinical follow-up on positive laboratory reports for the purpose of case classification. In areas with sustained high incidence of the disease, this resource-intensive activity yields a limited benefit to public health practice. A range of burden-reducing strategies have been implemented in many states, creating inconsistencies that limit the ability to decipher trends. Laboratory-based surveillance, or surveillance based solely on positive laboratory reports without follow-up for clinical information on positive laboratory reports, emerged as a feasible alternative to improve standardization in already high-incidence areas. To inform expectations of a laboratory-based surveillance model, we conducted a retrospective analysis of Lyme disease data collected during 2012-2018 from 10 high-incidence states. The number of individuals with laboratory evidence of infection ranged from 1302 to 20,994 per state and year. On average, 55% of those were ultimately classified as confirmed or probable cases (range: 29%-86%). Among all individuals with positive laboratory evidence, 18% (range: 2%-37%) were determined to be 'not a case' upon investigation and 23% (range: 2%-52%) were classified as suspect cases due to lack of associated clinical information and thus were not reported to the Centers for Disease Control and Prevention (CDC). The number of reported cases under a laboratory-based approach to surveillance in high-incidence states using recommended two-tier testing algorithms is likely to be, on average, 1.2 times higher (range: 0.6-1.8 times) than what was reported to CDC during 2012-2018. A laboratory-based surveillance approach for high-incidence states will improve standardization and reduce burden on public health systems, allowing public health resources to focus on prevention messaging, exploration of novel prevention strategies and alternative data sources to yield information on the epidemiology of Lyme disease.
(© 2022 Wiley-VCH GmbH. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.)