Alcohol consumption and ischemic heart disease mortality: are time-series correlations meaningful?

Recently, time-series correlations of aggregated data have been used to demonstrate the length of latency periods for environmental factors, such as economic conditions and alcohol consumption, in influencing heart disease mortality. Latency periods were specified by lagging mortality rates relative to the economic indicators or rates of alcohol consumption until the highest correlations were achieved. The tendency has been to interpret these correlations without regard to whether the latency periods described are biologically plausible. The authors have identified four models which represent all the possible outcomes of correlational studies of time-series data. Using United States and Canadian mortality rates in relationship to alcohol consumption, they have demonstrated the application of each of these models. For three of the four models, the time-series (lag) correlations are uniform regardless of the number of years mortality is lagged relative to alcohol consumption, and this uniformity does not permit a latency period to be identified. Only the lag correlations between two nonlinear variables show variations over time, depending on the degree of correspondence between the increasing and decreasing line segments of the two curves. Correlations ranging from high positive to high negative are possible, and several peak correlations (positive and negative) can occur. However, the biologic interpretation of multiple peaks with the same or different signs is problematic. The authors conclude that time-series correlations of aggregated data are not useful for the study of latency periods, and that analysis of time-series correlations for this purpose can be at best ambiguous, and at worst, completely misleading.