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Predicting population level hip fracture risk: a novel hierarchical model incorporating probabilistic approaches and factor of risk principles

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  • Daniel R. Martel
  • Martin Lysy
  • Andrew C. Laing

Abstract

Fall-related hip fractures are a major public health issue. While individual-level risk assessment tools exist, population-level predictive models could catalyze innovation in large-scale interventions. This study presents a hierarchical probabilistic model that predicts population-level hip fracture risk based on Factor of Risk (FOR) principles. Model validation demonstrated that FOR output aligned with a published dataset categorized by sex and hip fracture status. The model predicted normalized FOR for 100000 individuals simulating the Canadian older-adult population. Predicted hip fracture risk was higher for females (by an average of 38%), and increased with age (by15% per decade). Potential applications are discussed.

Suggested Citation

  • Daniel R. Martel & Martin Lysy & Andrew C. Laing, 2020. "Predicting population level hip fracture risk: a novel hierarchical model incorporating probabilistic approaches and factor of risk principles," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 23(15), pages 1201-1214, November.
    • Handle: RePEc:taf:gcmbxx:v:23:y:2020:i:15:p:1201-1214
    DOI: 10.1080/10255842.2020.1793331
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