Methods for Confounding Adjustment in Time Series Data: Applications to Short Term Effects of Air Pollution on Respiratory Health
This work explores methods for confounding adjustment in time series models. First, we will offer an analysis of daily time-series of asthma-prescriptions counts for Chicago's Medicaid population (aggregated at the ZIP-code level), as a function of air pollution during four summers. We employ a Bayesian hierarchical semi-parametric Poisson regression model, a generalized additive model where adjustment for confounding is implemented via a time-varying smooth function, which accounts for the slowly varying unobserved confounders. This function is represented via a pre-specified number of spline bases. We specify the prior distribution for the variance of basis coefficients (qsmoothness parameterq) to control the amount of confounding adjustment. In this manner, we are also able to account for the uncertainty regarding how much confounding adjustment is performed.1.2 Description of the Air Pollution and Asthma Study 1.2.1 Asthma Prescription Data The respiratory health data used in ... claim with asthma-related codes DRG 096, 097, or 098 or with at least one ICD-9 code of the form 493. xx (asthma).
| Title | : | Methods for Confounding Adjustment in Time Series Data: Applications to Short Term Effects of Air Pollution on Respiratory Health |
| Author | : | Chava Zibman |
| Publisher | : | ProQuest - 2008 |
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