Abstract #301075

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JSM 2003 Abstract #301075
Activity Number: 330
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract - #301075
Title: Nonparametric Trend Analysis of National Electronic Injury Surveillance System (Longitudinal Survey Sample) Data
Author(s): C. Craig Morris*+
Companies: Bureau of Transportation Statistics
Address: 400 Seventh St., SW, Washington, DC, 20590-0001,
Keywords: Mann-Kendall test ; trend analysis ; longitudinal survey sample ; power ; injury ; NEISS
Abstract:

The National Electronic Injury Surveillance System (NEISS) monitors emergency-room-treated injuries in the U.S. via a longitudinal stratified sample of emergency-room hospitals open 24 hours with at least six beds. Staff at each participating hospital review all emergency-room medical reports at the hospital and use a computer linked to the NEISS to code victim age and gender, injured body part, diagnosis, treatment disposition, treatment date, cause of injury, and other factors. Limitations of NEISS data include a small sample size (66 hospitals among five strata in the general NEISS), attrition, nonresponse, hospital mergers, periodic revisions of the sample, sparse injury counts, outliers, missing data, seasonality, and autocorrelation. These limitations make parametric trend analysis tenuous. In contrast, nonparametric methods related to the Mann-Kendall test, now commonly applied in the environmental sciences, appear suitable for parsimonious analysis of such data. The present paper demonstrates the power and robustness of approaches related to the Mann-Kendall test for trend analysis of NEISS data using Monte Carlo simulations and transportation-related injury data.

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