title: Validation of a non-linear model of health.
abstract: OBJECTIVE
  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.
authors.affiliation: University of Massachusetts Medical School, Shelburne Falls, MA, USA.
authors.forename: Stefan
authors.initials: S
authors.lastname: Topolski
authors.forename: Joachim
authors.initials: J
authors.lastname: Sturmberg
mesh_terms: D000328:Adult; D000368:Aged; D000375:Aging / Q000502:physiology; D002423:Cause of Death; D002648:Child; D016208:Databases, Factual; D005260:Female; D006262:Health; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D009010:Monte Carlo Method; D009026:Mortality / Q000639:trends*; D017711:Nonlinear Dynamics
keywords: chaos; complex systems; complexity; computer modelling; entropy; epidemiology; health; illness; information theory; mortality; philosophy of medicine; probability; theory