Epidemiology of Lyme disease in Pennsylvania 2006-2014 using electronic health records

Katherine A Moon; Jonathan Pollak; Annemarie G Hirsch; John N Aucott; Cara Nordberg; Christopher D Heaney; Brian S Schwartz

doi:10.1016/j.ttbdis.2018.10.010

Epidemiology of Lyme disease in Pennsylvania 2006-2014 using electronic health records

Ticks Tick Borne Dis. 2019 Feb;10(2):241-250. doi: 10.1016/j.ttbdis.2018.10.010. Epub 2018 Oct 26.

Authors

Katherine A Moon¹, Jonathan Pollak², Annemarie G Hirsch³, John N Aucott⁴, Cara Nordberg⁵, Christopher D Heaney⁶, Brian S Schwartz⁷

Affiliations

¹ Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: kmoon9@jhu.edu.
² Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: jpollak2@jhu.edu.
³ Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Epidemiology and Health Services Research, Geisinger, Danville, PA, USA. Electronic address: aghirsch@geisinger.edu.
⁴ Johns Hopkins School of Medicine, Department of Medicine, Division of Rheumatology, Baltimore, MD, USA. Electronic address: jaucott2@jhmi.edu.
⁵ Department of Biomedical and Translational Informatics, Geisinger, Danville, PA, USA. Electronic address: cmnordberg@geisinger.edu.
⁶ Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. Electronic address: cheaney1@jhu.edu.
⁷ Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA; Department of Epidemiology and Health Services Research, Geisinger, Danville, PA, USA; Johns Hopkins School of Medicine, Department of Medicine, Baltimore, MD, USA. Electronic address: bschwar1@jhu.edu.

PMID: 30420251
DOI: 10.1016/j.ttbdis.2018.10.010

Abstract

Lyme disease is the most common vector-borne disease in the United States. Electronic health record (EHR)-based research on Lyme disease is limited. We used Geisinger EHR data from 479,344 primary care patients in 38 Pennsylvania counties in 2006-2014 to compare EHR-based Lyme disease incidence rates to surveillance incidence rates, evaluate individual and community risk factors for incident Lyme disease, and to characterize the proportion of cases with diagnoses consistent with post-treatment Lyme disease syndrome in the EHR (PTLDS_EHR). We primarily identified Lyme disease cases using diagnosis codes, serologic testing order codes, and medication orders but also completed subgroup analyses among those with positive serology and those with both diagnosis code and antibiotic treatment. We compared annual incidence rates from the EHR to surveillance by age, sex, and county. In case-control analyses, we compared cases to randomly selected controls (5:1) frequency-matched on year, age, and sex. We identified 9657 cases of Lyme disease, including 1791 cases with positive serology and 4992 cases with both diagnosis code and antibiotic treatment. Annual incidence rates in the EHR were 4.25-7.43 times higher than surveillance. In adjusted analyses, white non-Hispanic race/ethnicity (vs. black, Hispanic, or other) was associated with higher odds of Lyme disease (odds ratio [OR]: 2.06, 95% confidence interval [CI]: 1.73-2.44). Medical Assistance insurance use (always vs. never; OR: 0.77, 95% CI: 0.68-0.88), and higher community-level socioeconomic deprivation (quartile 4 vs. 1 OR: 0.50 (95% CI: 0.42-0.59) were associated with lower odds of Lyme disease. Within 4-52 weeks after Lyme disease diagnosis, 20.8% (n = 735) of cases with a diagnosis code and treatment had a diagnosis of malaise or fatigue, pain, or cognitive difficulties not present in the past 26 weeks. These results highlight the utility of EHR data for epidemiologic research on Lyme disease for case-finding, surveillance, risk factor evaluation, and characterization of PTLDS using EHR data.

Keywords: Electronic health record; Epidemiology; Lyme borreliosis; Lyme disease; Post-treatment Lyme disease syndrome; Surveillance.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adolescent
Adult
Aged
Case-Control Studies
Child
Child, Preschool
Electronic Health Records*
Epidemiological Monitoring*
Female
Humans
Incidence
Infant
Infant, Newborn
Lyme Disease / diagnosis
Lyme Disease / epidemiology*
Male
Middle Aged
Odds Ratio
Pennsylvania / epidemiology
Population Surveillance
Primary Health Care
Risk Factors
Serologic Tests
Social Class
Young Adult