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The methods used so far for the analysis of time changes in population healthsuffer from the lack of causality in their design. This results in problemswith their implementation and interpretation. Here the method is presented withcausality directly implemented in the design. This is done by, first, buildingup a dynamic model of population, postulating existence of Driving Force actingat subjects, while they move along their cohort lines, causing the changes oftheir substantial health indicators , State Variables, at rate proportional tothis Force. The correspondent rates , named Cohort Trends, or C-trends,describe health history in each birth cohort. Having initial value and C-trends, the model allows to calculate health level (the means of State Variables) ineach birth cohort, and thus, in the whole population. The task for statisticalmethod is to identify the dynamic model (evaluate C-trends and Initial values)using data from a set of consecutive independent cross-sectional surveys. Thisis done by an iterative algorithm, running multiple regression procedure ateach step, until the specified smoothing conditions are fulfilled. Thealgorithm can operate with surveys having different age ranges. Theillustrative example shows the results of analysis of Body Mass Index for men ,using 7 surveys in period 1972-2002 with age ranges 25-64 and 25-74. SinceC-Trend is proxy for Driving Force, the year - age pattern of C-Trends providesunbiased information for health authorities on efficiency of health promotionactions or negative effects of uncontrolled harmful factors.

Dynamic Modelling of Health and its application to the large scale analysis of Body Mass Index, using data from consecutive set of surveys