Predicting admissions for childhood XXXX based on proximity to major roadways.

@entity1 @entity2789 @entity341 @entity565
PURPOSE: This retrospective study is an investigation of the relationship between traffic exposure and @entity2789 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 @entity1 with @entity565 . DESIGN: Using hospital and @entity341 ( @entity341 ) records, we investigated the relationship between proximity to major roadways and admissions for @entity565 exacerbations in the Fort Worth metropolitan area, designated as not attaining federal air health standards. The sample included 2,357 @entity1 from 1 to 12 years of age admitted for emergency or inpatient treatment in a 288-bed, nonprofit @entity1 '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 @entity1 consent. Student's t test was used to compare groups with and without primary @entity565 diagnosis on admission in respect to distance from major roadways. Logistic regression was used to model relationships between @entity565 admission and @entity1 ' characteristics, exposure to traffic, and social environment. FINDINGS: Controlling for several demographic factors, @entity565 occurrences were positively related to traffic exposures. On average, @entity1 with @entity565 lived closer to major roadways than did @entity1 who did not have @entity565 . @entity1 with @entity565 also tended to live in neighborhoods with more roads than did those who did not have @entity565 ; 3/4 of the @entity1 admitted for @entity565 during the study period and less than 1/3 of the @entity1 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. CLINICAL RELEVANCE: Knowledge of risk factors and their relative magnitudes enable nurses to better tailor symptom management for individual @entity1 .
