A model of functional thyroid disease status over the lifetime

Area of Science:

• Endocrinology
• Mathematical Biology
• Epidemiology

Background:

• Thyroid function impacts overall physiology, necessitating accurate disease modeling.
• Existing models may not fully capture the lifetime dynamics of functional thyroid disease.
• Understanding disease natural history is crucial for public health simulations.

Purpose of the Study:

• To develop and validate a Markov chain model for functional thyroid disease status over a lifetime.
• To estimate incidence rates of thyroid dysfunction in the U.S. population.
• To create a tool for population simulations and bias analyses in epidemiological studies.

Main Methods:

• Developed a seven-state Markov chain model representing functional thyroid disease status.
• Employed a Bayesian approach for parameter fitting using published data and meta-analysis.
• Utilized National Health and Nutrition Examination Survey (NHANES) 2007-2012 prevalence data for model calibration.
• Validated model predictions against observed prevalence data across sex and age groups.

Main Results:

• The fitted model demonstrated a satisfactory fit to observed thyroid disease prevalence data.
• Predicted incidence rates for treated thyroid disease showed good agreement with published values (82% within a 4-fold difference).
• Model-derived incidence rate differences were consistent with national iodine intake variations.
• Generated comprehensive estimates of functional thyroid disease incidence rates for the U.S.

Conclusions:

• The developed Markov chain model accurately represents functional thyroid disease natural history.
• The model is suitable for simulating U.S. populations and performing quantitative bias analyses.
• This tool enhances our understanding of thyroid disease epidemiology and its associations with environmental factors.