Using regression analysis to predict emergency patient volume at the Indianapolis 500 mile race

Ann Emerg Med. 1992 Oct;21(10):1200-3. doi: 10.1016/s0196-0644(05)81746-9.


Background: Emergency physicians often plan and provide on-site medical care for mass gatherings. Most of the mass gathering literature is descriptive. Only a few studies have looked at factors such as crowd size, event characteristics, or weather in predicting numbers and types of patients at mass gatherings.

Purpose: We used regression analysis to relate patient volume on Race Day at the Indianapolis Motor Speedway to weather conditions and race characteristics.

Methods: Race Day weather data for the years 1983 to 1989 were obtained from the National Oceanic and Atmospheric Administration. Data regarding patients treated on 1983 to 1989 Race Days were obtained from the facility hospital (Hannah Emergency Medical Center) data base. Regression analysis was performed using weather factors and race characteristics as independent variables and number of patients seen as the dependent variable. Data from 1990 were used to test the validity of the model.

Results: There was a significant relationship between dew point (which is calculated from temperature and humidity) and patient load (P less than .01). Dew point, however, failed to predict patient load during the 1990 race. No relationships could be established between humidity, sunshine, wind, or race characteristics and number of patients.

Conclusion: Although higher dew point was associated with higher patient load during the 1983 to 1989 races, dew point was a poor predictor of patient load during the 1990 race. Regression analysis may be useful in identifying relationships between event characteristics and patient load but is probably inadequate to explain the complexities of crowd behavior and too simplified to use as a prediction tool.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Automobiles
  • Emergency Medical Services / statistics & numerical data*
  • Forecasting / methods
  • Health Planning
  • Humans
  • Humidity
  • Indiana
  • Models, Theoretical
  • Recreation*
  • Regression Analysis
  • Temperature
  • Weather