Coherence: a new approach for analyzing interrelated serial data

Background Serial public health data may or may not reflect living conditions, political background and/or certain targeted health interventions. Yet, the effect of political events and/or health interventions that only last for a few years or a single legislative period may be difficult to analyze as restricting the range of the variables, i.e. the time interval within which the data were obtained, restricts the power of correlation analyzes. Objectives to provide a method to eliminate linear trends from serially obtained data and to visualize agreement between these data. Method We combine information of both the X- and the Y-axis and assess the agreement (coherence) between variables by clockwise (positive correlation) or anticlockwise (negative correlation) rotation of the coordinates. We provide an illustrative historic example of the coherence of infant mortality as an indicator of public health and body height in Germany between 1885 and 1995. Results Calculating coherence between correlating variables eliminates linear trends and leaves residuals that correspond to local correlation coefficients. The substantial changes in the coherence pattern of infant mortality and height exemplify the changes in the interaction between these variables during the transition from late feudalism to modern democracy. Conclusion Assessing coherence patterns enables a sensitive assessment of inhomogeneity and temporal trend changes in serially obtained correlated variables.