Public health

Sociology

Teenage pregnancy

Population

Mathematics

Biology

Statistics

Machine learning

Nursing

Developmental psychology

Biostatistics

Psychology

Dropout (neural networks)

Pregnancy

Computer science

Socioeconomics

School dropout

Bayesian network

Genetics

Medicine

Environmental health

BMC Public Health

Background As reported by the World Health Organization, adolescent pregnancy is a major public health concern given its impact on the life of mothers and their family members. In this study we investigated possible cause-effect relations between teenage pregnancy and school dropout, and other attributes that gravitate around them, using the Bayesian network approach. Methods We used a database prepared by the Adolescent House Project and invited experts in the areas of Health, Education and Social Assistance to answer a survey containing questions aimed at detecting possible causal relationships. To perform the statistical analysis and the numerical simulations we employed the language and formalism of Bayesian networks. Results The analysis indicated a strong cause-effect relation between teenage pregnancy and school dropout, bolstered by economic vulnerability. We were able to identify the profile of the female teenager who drops out from school: white girls older than 15 years who got pregnant at least once, are not working to generate an income, and who belong to the group where the family income is less than or equal to US$780 per month. Also we detected the “maternal impact factor", i.e., the effect caused by whether or not the mothers of the teenagers have experienced teenage pregnancy. Conclusion There are many factors that lead teenagers to drop out of school; we confirmed not only the commonsense notion that pregnancy of the teenager is a major factor but found that a history of teenage pregnancy on the part of the mother is a major factor. Moreover, Bayesian networks emerged as an interesting mathematical framework to perform the statistical analysis.

The impact of teenage pregnancy on school dropout in Brazil: a Bayesian network approach