Understand the effect of environmental and structural migration factors on the sexual and reproductive health of youth in central Asia and Africa: a modelling study
Background Every year millions of young people between the ages of 10 and 24 years in
central Asia and Africa migrate to urban areas in search of better opportunities. Common
factors driving youth migration include environmental degradation, war and conflict, and lack
of economic opportunity and poverty. In collaboration with the United Nations Population
Fund (UNFPA), our aim was to study the effect of migration on the sexual and reproductive
health (SRH) needs of youth in Nairobi, Cairo, Beirut, and Tunis through the development of …
central Asia and Africa migrate to urban areas in search of better opportunities. Common
factors driving youth migration include environmental degradation, war and conflict, and lack
of economic opportunity and poverty. In collaboration with the United Nations Population
Fund (UNFPA), our aim was to study the effect of migration on the sexual and reproductive
health (SRH) needs of youth in Nairobi, Cairo, Beirut, and Tunis through the development of …
Background
Every year millions of young people between the ages of 10 and 24 years in central Asia and Africa migrate to urban areas in search of better opportunities. Common factors driving youth migration include environmental degradation, war and conflict, and lack of economic opportunity and poverty. In collaboration with the United Nations Population Fund (UNFPA), our aim was to study the effect of migration on the sexual and reproductive health (SRH) needs of youth in Nairobi, Cairo, Beirut, and Tunis through the development of technological solutions for data collection and identification of youth most vulnerable to SRH issues.
Methods
We designed a web application useful for both migrants and researchers that contains a 27-question survey to collect migrant data on basic profile metrics such as their geographical journey and their sexual relations. This data can be used to inform the UNFPA's service, delivery, and policy efforts. The website also features a geographical healthcare centres map and safety tips for migrants. Additionally, we artificially created a dataset of 1000 migrant profiles to train a machine learning model to predict SRH vulnerabilities in young people.
Findings
The neural network model and the support vector machines algorithm demonstrated high accuracies of 0·92 and 0·90 respectively in predicting the most vulnerable youth. We optimised our algorithm to minimise false-negatives, even at the cost of a few false-positives.
Interpretation
Our results provide an opportunity to identify vulnerable youth for local officials to potentially reach out to in order to prevent SRH issues and to understand how such vulnerabilities might stem from their environmental and structural migration factors. Furthermore, researchers can investigate based on data collected from the web application how overcrowding of migrants in urban areas outstrips the areas of resources to further compromise the SRH health of the migrants.
Funding
None.
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