Defining thirds of schooling years in population studies

Area of Science:

• Epidemiology
• Public Health
• Demography

Background:

• Educational attainment has significantly increased throughout the 20th century.
• Comparing educational data across birth cohorts requires methods that account for these societal changes.
• International population studies like the WHO MONICA Project and MORGAM Project face challenges in analyzing educational trends.

Purpose of the Study:

• To develop and present a novel algorithm for categorizing individuals into three groups based on schooling years.
• To ensure the algorithm preserves smooth transitions in educational cut-points between consecutive birth years.
• To demonstrate the algorithm's utility using diverse datasets from Finland, Italy, Lithuania, and Scotland.

Main Methods:

• An algorithm was developed to divide individuals into three schooling year groups.
• The method focuses on maintaining smooth cut-point behavior across adjacent birth years.
• The algorithm's application was tested on population data from four European countries.

Main Results:

• The algorithm successfully categorized individuals into schooling year tertiles.
• Smoothness of cut-points between birth years was preserved by the method.
• Distinct patterns in estimated schooling year tertiles were observed across the studied populations.

Conclusions:

• The presented algorithm offers a robust approach for analyzing educational attainment in large-scale, longitudinal population studies.
• This method facilitates more accurate comparisons of schooling years across different birth cohorts and international contexts.
• The findings highlight the importance of accounting for educational trends when interpreting health data from diverse populations.