Functional data modelling approach for analysing and predicting trends in incidence rates--an application to falls injury

Abstract

Summary: Policy decisions about the allocation of current and future resources should be based on the most accurate predictions possible. A functional data analysis (FDA) approach improves the understanding of current trends and future incidence of injuries. FDA provides more valid and reliable long-term predictions than commonly used methods.

Introduction: Accurate information about predicted future injury rates is needed to inform public health investment decisions. It is critical that such predictions derived from the best available statistical models to minimise possible error in future injury incidence rates.

Methods: FDA approach was developed to improve long-term predictions but is yet to be widely applied to injury epidemiology or other epidemiological research. Using the specific example of modelling age-specific annual incidence of fall-related severe head injuries of older people during 1970-2004 and predicting rates up to 2024 in Finland, this paper explains the principles behind FDA and demonstrates their superiority in terms of prediction accuracy over the more commonly reported ordinary least squares (OLS) approach.

Results: Application of the FDA approach shows that the incidence of fall-related severe head injuries would increase by 2.3-2.6-fold by 2024 compared to 2004. The FDA predictions had 55% less prediction error than traditional OLS predictions when compared to actual data.

Conclusions: In summary, FDA provides more accurate predictions of long-term incidence trends than commonly used methods. The production of FDA prediction intervals for future injury incidence rates gives likely guidance as to the likely accuracy of these predictions.