About the COMO project

1
1

press to zoom
2
2

press to zoom
9
9

Describe your image

press to zoom
1
1

press to zoom
1/9

Childhood Obesity in Mexico

Childhood overweight and obesity rates have been increasing in the population <19 years. According to the latest results from the National Health and Nutrition Survey 2018 (ENSANUT 2018) estimate that in Mexico, 8.2% of infants (0-4 years), 35.6% of children (5-11 years) and almost 40% of the adolescents (12-19 years) present overweight or obesity. 

It is well known that childhood obesity has several immediate, intermediate, and long-term health consequences. Children and adolescents with overweight and obesity are likely to maintain their weight status into adulthood. They are at higher risks for developing chronic diseases, contributing then to increased morbidity or premature mortality. Besides health consequences, there are also economic consequences for individuals, families, and societies. 

Because of the rising levels in obesity in Mexico, every action measuring or attempting to tackle obesity in Mexico should be acknowledged. Any effort should be considered an experiment, were effectiveness needs to be documented for the benefit of every other initiative or strategy. Furthermore, evidence-based research and policies are needed to improve childhood wellbeing in Mexico. 

The COMO project

The "Childhood and adolescent Obesity in MexicO: evidence, challenges and opportunities” (COMO) Project intend to synthesise and use available data to comprehend the extent, nature, effects, and costs of childhood or adolescent obesity in Mexico. This project also looks to identify interventions done to revert or tackle obesity and effectiveness indicators. 

The COMO project looks to be a collaborative effort that uses an evidence-based approach to create a central database with relevant information regarding childhood obesity in Mexico. To know in more detail about the methodology, please have a look here

This project comprises five phases: 

1) Collection of scientific data (up to 2020, more than 850 relevant references have been identified) 

 

2) Collection grey literature

 

3) Collection of data sets

 

4) Comprehensive 

synthesis and analysis of data

5) Implementation  of machine learning methodologies for automation of phases 1-4