Risk factors for Lyme disease stage and manifestation using electronic health records

BMC Infectious Diseases

Katherine A. Moon¹  Jonathan S. Pollak¹  Annemarie G. Hirsch²  Christopher D. Heaney³  Brian S. Schwartz⁴  Melissa N. Poulsen⁵ 

[1] Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St., W7604, Baltimore, MD, USA;Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St., W7604, Baltimore, MD, USA;Department of Population Health Sciences, Geisinger, Danville, PA, USA;Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St., W7604, Baltimore, MD, USA;Department of Population Health Sciences, Geisinger, Danville, PA, USA;Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA;Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St., W7604, Baltimore, MD, USA;Department of Population Health Sciences, Geisinger, Danville, PA, USA;Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA;Department of Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA;Department of Population Health Sciences, Geisinger, Danville, PA, USA;

关键词: Lyme disease;    Tick-borne disease;    Electronic health records;    Epidemiology;    Disease stage;    Disseminated Lyme disease;   

DOI  :  10.1186/s12879-021-06959-y

来源: Springer

【 摘 要 】

BackgroundLittle is known about risk factors for early (e.g., erythema migrans) and disseminated Lyme disease manifestations, such as arthritis, neurological complications, and carditis. No study has used both diagnoses and free text to classify Lyme disease by disease stage and manifestation.MethodsWe identified Lyme disease cases in 2012–2016 in the electronic health record (EHR) of a large, integrated health system in Pennsylvania. We developed a rule-based text-matching algorithm using regular expressions to extract clinical data from free text. Lyme disease cases were then classified by stage and manifestation using data from both diagnoses and free text. Among cases classified by stage, we evaluated individual, community, and health care variables as predictors of disseminated stage (vs. early) disease using Poisson regression models with robust errors. Final models adjusted for sociodemographic factors, receipt of Medical Assistance (i.e., Medicaid, a proxy for low socioeconomic status), primary care contact, setting of diagnosis, season of diagnosis, and urban/rural status.ResultsAmong 7310 cases of Lyme disease, we classified 62% by stage. Overall, 23% were classified using both diagnoses and text, 26% were classified using diagnoses only, and 13% were classified using text only. Among the staged diagnoses (n = 4530), 30% were disseminated stage (762 arthritis, 426 neurological manifestations, 76 carditis, 95 secondary erythema migrans, and 76 other manifestations). In adjusted models, we found that persons on Medical Assistance at least 50% of time under observation, compared to never users, had a higher risk (risk ratio [95% confidence interval]) of disseminated Lyme disease (1.20 [1.05, 1.37]). Primary care contact (0.59 [0.54, 0.64]) and diagnosis in the urgent care (0.22 [0.17, 0.29]), compared to the outpatient setting, were associated with lower risk of disseminated Lyme disease.ConclusionsThe associations between insurance payor, primary care status, and diagnostic setting with disseminated Lyme disease suggest that lower socioeconomic status and less health care access could be linked with disseminated stage Lyme disease. Intervening on these factors could reduce the individual and health care burden of disseminated Lyme disease. Our findings demonstrate the value of both diagnostic and narrative text data to identify Lyme disease manifestations in the EHR.

【 授权许可】

CC BY