Trajectories of changes over twelve years in the health status of Canadians from late middle age

Aging in a given individual can be characterized by the number of deficits (symptoms, signs, laboratory abnormalities, disabilities) that they accumulate. The number of accumulated deficits, more than their nature, well characterizes health status in individuals — the proportion of deficits present in an individual to deficits considered is known as a frailty index. While on average deficits accumulate with age, individual trajectories in the number of deficits is highly dynamic. Transitions in the number of deficits over a fixed time interval can be represented by the Poisson law, with the Poisson mean dependent on the deficit numbers at baseline. Here we present an extension of the model to make possible predictions for any given time period. Using data from the Canadian National Population Health Survey of people aged 55 and over (n = 4330), followed during 7 cycles being the baseline and 6 cycles of follow-up every 2 years, we found that the transition in the number of deficits during any time period can be approximated using a time dependent Poisson distribution with the Poisson mean tending to decelerate over time, according to square-root-of-time kinetics characteristic for stochastic processes (e.g. diffusion, Brownian motion ) while the probability of death shows a pattern of time acceleration with a high degree of precision, “explaining” over 98% of variance. The model predicts a variety of changes in health status including the possibility of health improvement indicating the repair/remodeling abilities of the organism. The model is valuable for estimating how changes in health can influence mortality across the life course from late middle age.

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► We modeled the 12-year course of changes in deficit accumulation from late middle age.
► The law governing these changes is approximated by a time dependent Poisson distribution.
► The Poisson mean shows square-root-of time kinetics, typical for stochastic processes.
► The probability of death characteristically accelerates with time.
► The model predicts a variety of changes in health status, including improvement.