Predicting admissions for childhood asthma based on proximity to major roadways.
OBJECTIVE
This retrospective study is an investigation of the relationship between traffic exposure and childhood asthma exacerbations in a previously unstudied geographic area. We hypothesized that, controlling for selected demographic and social factors, exposure to traffic emissions would allow the prediction of hospital utilization for children with asthma.


METHODS
Using hospital and emergency department (ED) records, we investigated the relationship between proximity to major roadways and admissions for asthma exacerbations in the Fort Worth metropolitan area, designated as not attaining federal air health standards. The sample included 2,357 children from 1 to 12 years of age admitted for emergency or inpatient treatment in a 288-bed, nonprofit children's medical center in Fort Worth, Texas from January 1, 2004 to December 31, 2005. Data were analyzed using GIS mapping and logistic regression.


METHODS
Deidentification data were collected from hospital databases after IRB approval and waiver of parental permission or patient consent. Student's t test was used to compare groups with and without primary asthma diagnosis on admission in respect to distance from major roadways. Logistic regression was used to model relationships between asthma admission and patients' characteristics, exposure to traffic, and social environment.


RESULTS
Controlling for several demographic factors, asthma occurrences were positively related to traffic exposures. On average, patients with asthma lived closer to major roadways than did patients who did not have asthma. Patients with asthma also tended to live in neighborhoods with more roads than did those who did not have asthma; 3/4 of the children admitted for asthma during the study period and less than 1/3 of the children admitted for nonasthma diagnoses lived within 1,500 meters of a major roadway (p=.0001). Controlling for other factors, every meter increase in proximity to major roadways produced 0.1% increase in likelihood of admission.


CONCLUSIONS
Knowledge of risk factors and their relative magnitudes enable nurses to better tailor symptom management for individual patients.
Newcomb
Patricia
P
University of Texas-Arlington, School of Nursing, Arlington, TX, USA. pnewcomb@uta.edu
Li
Jianling
J
D001249:Asthma / Q000209:etiology / Q000503:physiopathology*; D002648:Child; D004781:Environmental Exposure; D006801:Humans; D010343:Patient Admission / Q000706:statistics & numerical data; D012111:Residence Characteristics; D012307:Risk Factors; D001335:Vehicle Emissions
D001335:Vehicle Emissions