Estimating the Effect of Dust Events on Daily Hospitalizations for Asthma While Adjusting for Hourly Levels of Air Pollutants with the Historical Functional Linear Model

• Activity Number: 187
• Type: Topic Contributed
• Date/Time: Monday, August 1, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Priyangi Kanchana Bulathsinhala*+ and Joan Staniswalis and Sara Grineski
• Companies: The University of Texas at El Paso and The University of Texas at El Paso and The University of Texas at El Paso
• Keywords: Nonparametric functional linear model ; Case-crossover design ; Conditional logistic regression

Abstract

El Paso, Texas is known as one of the dust "hotspots" in North America. We explore the effect of dust storms on asthma admissions in El Paso, Texas between 2000 and 2005. Conditional logistic regression with a case-crossover design was used to estimate the probability of hospitalization during dust events while controlling for pollutants with hourly monitor measurements, and weather. The functional linear model is used to incorporate the hourly pollutant measures into the regression model with a continuous lag, as an alternative to a distributed lag model based on daily averages. The nonparametric functional linear model in the conditional logistic regression framework is fit by first preprocessing the data, then applying the COXPH function in the R-package for survival analysis with a slight modification. We use the ridge trace to guide the choice of the smoothing parameter in nonparametric functional linear model. This is described and preliminary findings are reported.