Modelling youth pregnancy in continental Portugal through geographically weighted regression

  • João David | jcdavid@novaims.unl.pt NOVA Information Management School, NOVA University of Lisbon, Lisbon, Portugal.
  • Pedro Cabral NOVA Information Management School, NOVA University of Lisbon, Lisbon, Portugal.

Abstract

Youth pregnancy, a global public health problem with important social, educational and economic impact, has mostly been studied in the least developed countries. However, this condition also affects the industrialized countries. This article presents a youth pregnancy study at the municipality level in continental Portugal based on geographically weighted regression. The results indicate that youth pregnancy rates can be explained by several variables: i) proportion (%) of social security beneficiaries; ii) number of households without amenities; iii) the rate of those prematurely leaving school; iv) the unemployment rates of youths and females, ceteris paribus. In addition, it was found that the beneficiaries of social security had a higher impact on youth pregnancy in the southern part of the country, and in the Centre too; that households without amenities had a higher impact along the central coast and in the South; that rates of those leaving school prematurely had a higher influence in the North and the Interior than in the rest of the country; and that youth and female unemployment rates were more widespread in the Centre, particularly along the coast. Overall, the model identified a strong association of explanatory variables with youth pregnancy rates in the country as a whole, except in the Porto metropolitan area. These findings may help health planners to define policies to mitigate this important social problem.

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Published
2019-05-14
Section
Original Articles
Keywords:
Youth pregnancy, Spatial modelling, Geographically weighted regression, Non-stationarity, Geographical information systems, Portugal
Statistics
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How to Cite
David, J., & Cabral, P. (2019). Modelling youth pregnancy in continental Portugal through geographically weighted regression. Geospatial Health, 14(1). https://doi.org/10.4081/gh.2019.680