Public health application of predictive modeling: An example from farm vehicle crashes

Shabbar I. Ranapurwala, Joseph E. Cavanaugh, Tracy Young, Hongqian Wu, Corinne Peek-Asa, Marizen R. Ramirez

Research output: Contribution to journalArticlepeer-review

14 Scopus citations


Background: The goal of predictive modelling is to identify the likelihood of future events, such as the predictive modelling used in climate science to forecast weather patterns and significant weather occurrences. In public health, increasingly sophisticated predictive models are used to predict health events in patients and to screen high risk individuals, such as for cardiovascular disease and breast cancer. Although causal modelling is frequently used in epidemiology to identify risk factors, predictive modelling provides highly useful information for individual risk prediction and for informing courses of treatment. Such predictive knowledge is often of great utility to physicians, counsellors, health education specialists, policymakers or other professionals, who may then advice course correction or interventions to prevent adverse health outcomes from occurring. In this manuscript, we use an example dataset that documents farm vehicle crashes and conventional statistical methods to forecast the risk of an injury or death in a farm vehicle crash for a specific individual or a scenario. Results: Using data from 7094 farm crashes that occurred between 2005 and 2010 in nine mid-western states, we demonstrate and discuss predictive model fitting approaches, model validation techniques using external datasets, and the calculation and interpretation of predicted probabilities. We then developed two automated risk prediction tools using readily available software packages. We discuss best practices and common limitations associated with predictive models built from observational datasets. Conclusions: Predictive analysis offers tools that could aid the decision making of policymakers, physicians, and environmental health practitioners to improve public health.

Original languageEnglish (US)
Article number31
JournalInjury Epidemiology
Issue number1
StatePublished - Jun 17 2019

Bibliographical note

Funding Information:
This work was funded by the Great Plains Center for Agricultural Health which is funded through the National Institute for Occupational Health and Safety grant # U50 OH007548–11. The funding for publication was provided by the University of North Carolina at Chapel Hill Injury Prevention Research Center, which is funded by the Centers for Disease Control and Prevention grant #R49 CE002479.

Publisher Copyright:
© 2019 The Author(s).


  • Decision support techniques
  • Forecasting
  • Motor vehicles
  • Predictions


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