A multiphase method for estimating cohort effects in age-period contingency table data

Understanding the effects of age, period, and cohort on disease morbidity and mortality may help identify etiological factors and inform prevention programs. We illustrate a three-phase method that conceptualizes the cohort effect as a partial interaction between age and period. As an example of application, we analyze homicide mortality data for males in the United States from 1935 through 2004.

The three-phased method begins with graphical inspection; second, a median polish is used to remove the log-additive components of age and period effects; third, a linear regression of residuals from the median polish is modeled to quantify the relative magnitude of the cohort effect.

Individuals born after 1960 have a significantly increased rate of homicide relative to those born between 1920 and 1924. After removal of the log-additive effects of age and period, the estimated homicide rate for men born between 1980 and 1984 is more than twice the rate for men born between 1920 and 1924 (rate ratio, 2.11; 95% confidence interval, 1.98-2.25).

The three-phase method presented herein offers several advantages, the foremost being an alternative conceptualization of the cohort effect not as an independent component of age and period effects, but as a partial interaction. In addition, the strengths of the method include computational simplicity, interpretability, and reliability.