Validation of a non-linear model of health.

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.

For every complex problem, there is an answer that is clear, simple and wrong: and other aphorisms about medical statistical fallacies.

RATIONALE, METHOD: This essay examines the notions of knowledge, truth and certainty as they apply to medical research and patient care. The human body does not behave in mechanistic but rather complex adaptive ways; thus, its behaviour to challenges is non-deterministic. This insight has important ramifications for experimental studies in health care and their statistical interrogation that are described in detail. RESULTS AND CONCLUSIONS: Four implications are highlighted: one, there is an urgent need to develop a greater awareness of uncertainties and how to respond to them in clinical practice, namely, what is important and what is not in the context of this patient; two, there is an equally urgent need for health professionals to understand some basic statistical terms and their meanings, specifically absolute risk, its reciprocal, numbers needed to treat and its inverse, index of therapeutic impotence, as well as seeking out the effect size of an intervention rather than blindly accepting P-values; three, there is an urgent need to accurately present the known in comprehensible ways through the use of visual tools; and four, there is a need to overcome the perception, that errors of commission are less troublesome than errors of omission as neither's consequences are predictable.

Understanding health from a complex systems perspective.

Doctors often use theory to inform medical practice. The current bio-psycho-social model of health may be advanced still further with theoretical rigour. Traditional fields of thermodynamics and newer fields of non-linear dynamics including chaos theory and complex systems science can inform our understanding of the complexity of human health, illness and disease. Commonly accepted aspects of human health may be projected as probabilities over time creating curves of human health potential. Maximum health may be represented by maximum functional complexity. Complexity's relationship to entropy and energy can produce a complex surface that better models the human experience of health and illness from birth to death. Such a potential health trajectory uniting complexity and entropy expands upon earlier theories of health while allowing for unusual predictions and the novel opportunity to test and validate this model of human health.