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Multinomial additive hazard model to assess the disability burden using cross-sectional data.

Renata T C Yokota1,2, Herman Van Oyen1,3, Caspar W N Looman4

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Biometrical Journal. Biometrische Zeitschrift
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Summary

This study extends the attribution method to assess chronic disease contributions to multinomial disability in aging populations. The new model helps understand disease impact on quality of life and healthcare costs.

Keywords:
Additive hazard modelChronic diseaseConstrained optimizationCross-sectional dataCumulative rateDisabilityMultinomial likelihood

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Area of Science:

  • Gerontology
  • Epidemiology
  • Biostatistics

Background:

  • Population aging increases chronic diseases and disability, impacting elderly quality of life and healthcare costs.
  • Identifying key chronic diseases contributing to disability is crucial for effective burden reduction strategies.
  • Cross-sectional data analysis offers a cost-effective alternative to longitudinal studies for assessing disability causes.

Purpose of the Study:

  • To extend the attribution method for analyzing chronic disease contributions to multinomial disability outcomes.
  • To adapt existing methods for multicategory disability measures common in health surveys.
  • To provide a statistical tool for understanding the complex relationship between chronic diseases and disability severity.

Main Methods:

  • Extension of the attribution method to handle multinomial (multicategory) disability responses.
  • Utilized the R function `constrOptim` to maximize the multinomial log-likelihood function under linear inequality constraints.
  • Conducted a simulation study to evaluate the model's performance and identify potential limitations.

Main Results:

  • The proposed extended attribution method demonstrated good overall performance in simulations, with no convergence issues.
  • The model is robust but requires careful application in populations with low marginal disability probabilities and high conditional probabilities, especially with small sample sizes.
  • The method was successfully applied to real-world data from the Belgian Health Interview Surveys for illustrative purposes.

Conclusions:

  • The extended attribution method provides a valuable tool for quantifying the impact of chronic diseases on different levels of disability in aging populations.
  • This statistical approach aids in understanding disease burden and informing public health interventions for elderly care.
  • The study highlights the importance of considering multimorbidity and disability severity in epidemiological research using cross-sectional data.