Abstract

Purpose: The purpose of this study was to evaluate the veracity of a theoretically derived model of health that describes a non-linear trajectory of health from birth to death with available population data sets. Methods: The distribution of mortality by age is directly related to health at that age, thus health approximates 1/mortality. The inverse of available all-cause mortality data from various time periods and populations was used as proxy data to compare with the theoretically derived non-linear health model predictions, using both qualitative approaches and quantitative one-sample Kolmogorov–Smirnov analysis with Monte Carlo simulation. Results: The mortality data's inverse resembles a log–normal distribution as predicted by the proposed health model. The curves have identical slopes from birth and follow a logarithmic decline from peak health in young adulthood. A majority of the sampled populations had a good to excellent quantitative fit to a log–normal distribution, supporting the underlying model assumptions. Post hoc manipulation showed the model predictions to be stable. Conclusions: This is a first theory of health to be validated by proxy data, namely the inverse of all-cause mortality. This non-linear model, derived from the notion of the interaction of physical, environmental, mental, emotional, social and sense-making domains of health, gives physicians a more rigorous basis to direct health care services and resources away from disease-focused elder care towards broad-based biopsychosocial interventions earlier in life