Life course epidemiology: Modeling educational attainment with administrative data

BACKGROUND: Understanding the processes across childhood and adolescence that affect later life inequalities depends on many variables for a large number of individuals measured over substantial time periods. Linkable administrative data were used to generate birth cohorts and to study pathways of inequity in childhood and early adolescence leading to differences in educational attainment. Advantages and disadvantages of using large administrative data bases for such research were highlighted. METHODS: Children born in Manitoba, Canada between 1982 and 1995 were followed until age 19 (N = 89,763), with many time-invariant measures serving as controls. Five time-varying predictors of high school graduation—three social and two health—were modelled using logistic regression and a framework for examining predictors across the life course. For each time-varying predictor, six temporal patterns were tested: full, accumulation of risk, sensitive period, and three critical period models. RESULTS: Predictors measured in early adolescence generated the highest odds ratios, suggesting the importance of adolescence. Full models provided the best fit for the three time-varying social measures. Residence in a low-income neighborhood was a particularly influential predictor of not graduating from high school. The transmission of risk across developmental periods was also highlighted; exposure in one period had significant implications for subsequent life stages. CONCLUSION: This study advances life course epidemiology, using administrative data to clarify the relationships among several measures of social behavior, cognitive development, and health. Analyses of temporal patterns can be useful in studying such other outcomes as educational achievement, teen pregnancy, and workforce participation.