Walkability and socio-economic status in relation to walking, playing and sports practice in a representative Spanish sample of youth: The PASOS study.

PURPOSE: Physical activity (PA) provides multiple health-related benefits in children and adolescents, however, at present, the majority of young people are insufficiently physically active. The aim of this study was to evaluate if neighborhood walkability and/or socio-economic status (SES) could affect the practice of walking, play outdoors and sports practice in a representative sample of Spanish children and adolescents. METHODS: A sample of 4092 youth (aged 8-16 years old) from 245 primary and secondary schools in 121 localities from each of the 17 Spanish autonomous communities participated in the study. Walk Score was used to evaluate walkability of the neighborhood and household income was used as an indicator of SES. A 7-item self-reported validated questionnaire, was used to assess PA levels, and in a subsample of 10% of the participants, randomly selected from the entire sample, PA was objectively measured by accelerometers. RESULTS: Youth from more walkable areas reported more minutes walking per day compared with those from less walkable neighborhoods (51.4 vs 48.8 minutes, respectively). The lowest average minutes spent in playing outdoors was found among participants from low-SES and low-walkable neighborhoods. Neighborhood SES influenced on the participation in team sports during the weekend, being this participation higher in high SES neighborhoods. CONCLUSION: Providing high walkable environments seems a good strategy to promote PA regardless SES levels. It seems that improving the walkability is a key component to partially overcome the SES inequalities, especially in urban areas with low SES. High-SES environments can offer better sports facilities and more organized physical activities than low-SES ones.

iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.

Introduction: Malaria is one of the most prevalent infectious diseases in sub-Saharan Africa, with 247 million cases reported worldwide in 2021 according to the World Health Organization. Optical microscopy remains the gold standard technique for malaria diagnosis, however, it requires expertise, is time-consuming and difficult to reproduce. Therefore, new diagnostic techniques based on digital image analysis using artificial intelligence tools can improve diagnosis and help automate it. Methods: In this study, a dataset of 2571 labeled thick blood smear images were created. YOLOv5x, Faster R-CNN, SSD, and RetinaNet object detection neural networks were trained on the same dataset to evaluate their performance in Plasmodium parasite detection. Attention modules were applied and compared with YOLOv5x results. To automate the entire diagnostic process, a prototype of 3D-printed pieces was designed for the robotization of conventional optical microscopy, capable of auto-focusing the sample and tracking the entire slide. Results: Comparative analysis yielded a performance for YOLOv5x on a test set of 92.10% precision, 93.50% recall, 92.79% F-score, and 94.40% mAP0.5 for leukocyte, early and mature Plasmodium trophozoites overall detection. F-score values of each category were 99.0% for leukocytes, 88.6% for early trophozoites and 87.3% for mature trophozoites detection. Attention modules performance show non-significant statistical differences when compared to YOLOv5x original trained model. The predictive models were integrated into a smartphone-computer application for the purpose of image-based diagnostics in the laboratory. The system can perform a fully automated diagnosis by the auto-focus and X-Y movements of the robotized microscope, the CNN models trained for digital image analysis, and the smartphone device. The new prototype would determine whether a Giemsa-stained thick blood smear sample is positive/negative for Plasmodium infection and its parasite levels. The whole system was integrated into the iMAGING smartphone application. Conclusion: The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.

Independent and Joined Association between Socioeconomic Indicators and Pediatric Obesity in Spain: The PASOS Study.

Childhood obesity is a public health problem worldwide. An important determinant of child and adolescent obesity is socioeconomic status (SES). However, the magnitude of the impact of different SES indicators on pediatric obesity on the Spanish population scale is unclear. The aim of this study was to assess the association between three SES indicators and obesity in a nationwide, representative sample of Spanish children and adolescents. A total of 2791 boys and girls aged 8 to 16 years old were included. Their weight, height, and waist circumference were measured. SES was assessed using two parent/legal guardian self-reported indicators (educational level -University/non-University- and labor market status -Employed/Unemployed-). As a third SES indicator, the annual mean income per person was obtained from the census section where the participating schools were located (≥12.731€/<12.731€). The prevalence of obesity, severe obesity, and abdominal obesity was 11.5%, 1.4%, and 22.3%, respectively. Logistic regression models showed an inverse association of both education and labor market status with obesity, severe obesity, and abdominal obesity (all p < 0.001). Income was also inversely associated with obesity (p < 0.01) and abdominal obesity (p < 0.001). Finally, the highest composite SES category (University/Employed/≥12.731€ n = 517) showed a robust and inverse association with obesity (OR = 0.28; 95% CI: 0.16-0.48), severe obesity (OR = 0.20; 95% CI: 0.05-0.81), and abdominal obesity (OR = 0.36; 95% CI: 0.23-0.54) in comparison with the lowest composite SES category (Less than University/Unemployed/<12.731€; n = 164). No significant interaction between composite SES categories and age and gender was found. SES is strongly associated with pediatric obesity in Spain.