Integrating Spatial Epidemiology into a Decision Model for Evaluation of Facial Palsy in Children

Fine, Andrew M.; Brownstein, John S.; Nigrovic, Lise E.; Kimia, Amir A.; Olson, Karen L.; Thompson, Amy D.; Mandl, Kenneth D.

doi:10.1001/archpediatrics.2010.250

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Integrating Spatial Epidemiology into a Decision Model for Evaluation of Facial Palsy in Children

Published Date:

Jan 2011

Publisher's site:

http://dx.doi.org/10.1001/archpediatrics.2010.250 New Site Icon

Source:

Arch Pediatr Adolesc Med. 165(1):61-67.

[PDF-456.08 KB]

Details:

Personal Authors:

Fine, Andrew M. ; Brownstein, John S. ; Nigrovic, Lise E. ; Kimia, Amir A. ; Olson, Karen L. ; ... More ▼

Keywords:

[+]

Pubmed ID:

21199982

Pubmed Central ID:

PMC3644029

Description:

Objective

To develop a novel diagnostic algorithm for Lyme disease among children with facial palsy by integrating public health surveillance data with traditional clinical predictors.

Design

Retrospective cohort study.

Setting

Children’s Hospital Boston emergency department,1995–2007

Patients

264 children under age 20 years presenting with peripheral facial palsy who were evaluated for Lyme disease

Main outcome measures

Multivariate regression was used to identify independent clinical and epidemiologic predictors of Lyme facial palsy.

Results

65% of children from high-risk counties during Lyme season tested positive, compared to 5% of children without geographic or seasonal risk factors present. Among patients with both seasonal and geographic risk factors, 80% with one clinical risk factor (fever or headache) and 100% with two clinical factors had Lyme. Factors independently associated with Lyme facial palsy were presentation from June-November (odds ratio 25, 95% CI 8.3–113), residence in a county where the most recent three year average Lyme incidence exceeded 4 cases/100,000 (18, 6.5–69), fever (3.9, 1.5–11), and headache (2.7, 1.3–5.8). Clinical experts correctly treated 68/94 (72%) patients with Lyme facial palsy, but a tool incorporating geographical and seasonal risk identified all 94 cases.

Conclusions

Most clinicians intuitively integrate geographic information into Lyme disease management, but we demonstrate quantitatively how formal use of geographically-based incidence in a clinical algorithm improves diagnostic accuracy. These findings demonstrate potential for improved outcomes from investments in health information technology that foster bidirectional communication between public health and clinical settings.

Document Type:

Journal Article

Collection(s):

CDC Public Access

Funding:

G08 LM009778/LM/NLM NIH HHS/United States
G08LM009778/LM/NLM NIH HHS/United States
K01HK000055/HK/PHITPO CDC HHS/United States
P01HK000016/HK/PHITPO CDC HHS/United States
P01HK000088-01/HK/PHITPO CDC HHS/United States
R01 LM007677/LM/NLM NIH HHS/United States
R01 LM007677/LM/NLM NIH HHS/United States

Supporting Files:

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