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. 2021 Oct 13;21(1):1850.
doi: 10.1186/s12889-021-11878-3.

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

Emerson Cruz  1 , Fabio Gagliardi Cozman  2 , Wilson Souza  3 , Albertina Takiuti  4
Affiliations

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

Emerson Cruz et al. BMC Public Health. .

Abstract

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.

Keywords: Bayesian networks; Causality; School dropout; Teenage pregnancy.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
An example of a directed acyclic graph, where W, X, Y and Z are random variables; X and Z are exogenous variables while Y and W are endogenous variables
Fig. 2
Fig. 2
Intervention on X and adjustment for Z; X,Y and Z are random variables
Fig. 3
Fig. 3
The structure of the Bayesian network. Abbreviations are explained in Table 1
See this image and copyright information in PMC

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