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Quantifying non-communicable diseases' burden in Egypt using State-Space model.

Somaya El-Saadani1, Mohamed Saleh2, Sarah A Ibrahim1

  • 1Department of Biostatistics and Demography,Faculty of Graduate Studies for Statistical Research, Cairo University, Cairo, Egypt.

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This study modeled the health burden of four non-communicable diseases in Egypt, finding rising disease trajectories. Bayesian methods revealed risk factors, disease prevalence, and severity increase burden, while healthcare utilization unexpectedly decreased.

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Area of Science:

  • Public Health
  • Epidemiology
  • Health Economics

Background:

  • Non-communicable diseases (NCDs) pose a significant and growing health challenge globally, particularly in developing countries.
  • Quantifying the health burden of NCDs is crucial for effective public health policy and resource allocation.
  • Egypt faces a substantial burden from NCDs, necessitating accurate modeling and estimation.

Purpose of the Study:

  • To model and quantify the health burden of four major non-communicable diseases in Egypt.
  • To estimate the trajectories of NCD health burden using advanced statistical methods.
  • To identify key drivers and factors influencing the NCD health burden in a developing country context.

Main Methods:

  • Utilized the State-Space model framework for time-series analysis.
  • Employed two Bayesian estimation methods: Particle Filter and Particle Independent Metropolis-Hastings.
  • Integrated data from multiple sources including IHME, CAPMAS, World Bank, and WHO.

Main Results:

  • Both Bayesian methods indicated an increasing trend in the health burden trajectories of the studied NCDs.
  • Higher prevalence of risk factors, disease prevalence, and disease severity were associated with increased illness burden.
  • A counterintuitive negative correlation was observed between disease burden and healthcare service utilization.

Conclusions:

  • The Particle Independent Metropolis-Hastings method is effective for parameter estimation in State-Space models with time-constant parameters.
  • Findings suggest that factors beyond disease prevalence, such as healthcare system limitations and patient behavior, influence health outcomes.
  • Recommends the application of State-Space models with Bayesian approaches for analyzing public health and epidemiological time-series data.