Spatiotemporal predictions of obesity prevalence in Chinese children and adolescents: based on analyses of obesogenic environmental variability and Bayesian model

Geospatial Modeling of Deep Neural Visual Features for Predicting Obesity Prevalence in Missouri: Quantitative Study

BACKGROUND

The global obesity epidemic demands innovative approaches to understand its complex environmental and social determinants. Spatial technologies, such as geographic information systems, remote sensing, and spatial machine learning, offer new insights into this health issue. This study uses deep learning and spatial modeling to predict obesity rates for census tracts in Missouri.

OBJECTIVE

This study aims to develop a scalable method for predicting obesity prevalence using deep convolutional neural networks applied to satellite imagery and geospatial analysis, focusing on 1052 census tracts in Missouri.

METHODS

Our analysis followed 3 steps. First, Sentinel-2 satellite images were processed using the Residual Network-50 model to extract environmental features from 63,592 image chips (224×224 pixels). Second, these features were merged with obesity rate data from the Centers for Disease Control and Prevention for Missouri census tracts. Third, a spatial lag model was used to predict obesity rates and analyze the association between deep neural visual features and obesity prevalence. Spatial autocorrelation was used to identify clusters of obesity rates.

RESULTS

Substantial spatial clustering of obesity rates was found across Missouri, with a Moran I value of 0.68, indicating similar obesity rates among neighboring census tracts. The spatial lag model demonstrated strong predictive performance, with an R2 of 0.93 and a spatial pseudo R2 of 0.92, explaining 93% of the variation in obesity rates. Local indicators from a spatial association analysis revealed regions with distinct high and low clusters of obesity, which were visualized through choropleth maps.

CONCLUSIONS

This study highlights the effectiveness of integrating deep convolutional neural networks and spatial modeling to predict obesity prevalence based on environmental features from satellite imagery. The model's high accuracy and ability to capture spatial patterns offer valuable insights for public health interventions. Future work should expand the geographical scope and include socioeconomic data to further refine the model for broader applications in obesity research.

Application of compound poisson model to estimate underreported risk of non-communicable diseases in underdeveloped areas

Background

Hypertension and diabetes are major components of non-communicable diseases (NCDs), with a substantial number of patients residing in underdeveloped areas. Limited medical resources in these areas often results in underreporting of disease prevalence, masking the true extent of diseases. Taking the underdeveloped Liangshan Yi Autonomous Prefecture in China as an example, this study aimed to correct the underreported prevalence of hypertension and type 2 diabetes so as to provide inspiration for the allocation of medical resources in such areas.

Methods

Assuming the true number of patients in each area follows a Poisson distribution, we applied a Compound Poisson Model based on Clustering of Data Quality (CPM-CDQ) to estimate the potential true prevalence of hypertension and diabetes, as well as the registration rate of existing patients. Specifically, a hierarchical clustering approach was utilized to group the counties based on the data quality, and then the registration rate of the cluster with the best data quality was used as a priori information for the model. The model parameters were estimated by the maximum likelihood method. Sensitivity analyses were performed to test the robustness of the model.

Results

The estimated prevalence of hypertension in the entire Liangshan Prefecture from 2018 to 2020 ranged from 24.59 % to 25.28 %, and for diabetes, it ranged from 4.95 % to 8.42 %. The registration rates for hypertension and diabetes were 14.10 % to 24.59 % and 15.98 % to 29.12 %, respectively. Additionally, the accuracy of clustering the counties with the best data quality had a significant impact on the performance of the model.

Conclusion

Liangshan Prefecture is experiencing a significant high prevalence of hypertension and diabetes, accompanied by a concerningly low registration rate. The CPM-CDQ proved useful for assessing underreporting risks and facilitating targeted interventions for NCDs control and prevention, particularly in underdeveloped areas.

Prediction models for children/adolescents with obesity/overweight: A systematic review and meta-analysis

The incidence of obesity and overweight in children and adolescents is increasing worldwide and becomes a global health concern. This study aims to evaluate the accuracy of available prediction models in early identification of obesity and overweight in general children or adolescents and identify predictive factors for the models, thus provide a reference for subsequent development of risk prediction tools for obesity and overweight in children or adolescents. Related publications were obtained from several databases such as PubMed, Embase, Cochrane Library, and Web of Science from their inception to September 18th, 2022. The novel Prediction Model Risk of Bias Assessment Tool (PROBAST) was employed to assess the bias risk of the included studies. R4.2.0 and Stata15.1 softwares were used to conduct meta-analysis. This study involved 45 cross-sectional and/or prospective studies with 126 models. Meta-analyses showed that the overall pooled index of concordance (c-index) of prediction models for children/adolescents with obesity and overweight in the training set was 0.769 (95% CI 0.754-0.785) and 0.835(95% CI 0.792-0.879), respectively. Additionally, a large number of predictors were found to be related to children's lifestyles, such as sleep duration, sleep quality, and eating speed. In conclusions, prediction models can be employed to predict obesity/overweight in children and adolescents. Most predictors are controllable factors and are associated with lifestyle. Therefore, the prediction model serves as an excellent tool to formulate effective strategies for combating obesity/overweight in pediatric patients.

Spatial-temporal evolution of overweight and obesity among Chinese adolescents from 2016 to 2020

This study examines the spatial-temporal evolution of overweight and obesity among Chinese adolescents aged 14-17. Data from five national surveys conducted between 2016 and 2020 were analyzed to determine distribution patterns and trends. Results showed that overweight and obesity exhibit spatial clustering, with greater severity in the north and less severity in the south. The issue has spread from the northeast to the southwest of Mainland China. Using a local autocorrelation model, the regions were divided into a northern disease cold spot area (Inner Mongolia) and a southern disease hot spot area (Guangxi). Over the past five years, overweight rates among Chinese adolescents have not been effectively curbed, but obesity has shown some success in control and reversal until 2019. Future efforts should focus on the spatial-temporal pattern of disease spread, targeting hotspot areas and abnormal values for regional synergy and precise prevention and control.

Analysis of the gut microbiome in obese native Tibetan children living at different altitudes: A case-control study

Objective

To explore the relationship between intestinal flora and obesity in Tibetan children at different altitudes.

Methods

Using16S rRNA gene sequencing results and blood lipid metabolism indexes to study the characteristics of the intestinal flora present in faeces and changes in blood lipid metabolism in obese children in Tibet who reside at different altitudes and to study correlations between blood lipid metabolism indicators and the intestinal flora composition.

Results

The results showed the following. (a) The triglyceride (TG) and low-density lipoprotein cholesterol (LDL-C) levels in the obesity groups were higher than those in the normal-weight groups, and those in the high-altitude obesity groups were lower than those in the low-altitude obesity groups. (b) The 16S rRNA gene sequencing results showed that altitude affected the composition and relative abundance of the gut microbiota. These parameters were basically the same among the low-altitude groups, while they were significantly lower in the high-altitude groups than in the low-altitude groups. (c) Groups that lived at different altitudes and had different body weights had different dominant bacterial genera. Megamonas was closely related to obesity, and its relative abundance in the low-altitude groups was higher than that in the high-altitude groups. Prevotella was associated with altitude, and its relative abundance in the high-altitude groups was higher than that in the low-altitude groups. In addition, Prevotella elicited changes in the abundance of Escherichia-Shigella. The lower prevalence of obesity and incidence of intestinal inflammation in those living at high altitudes were related to the abundance of Prevotella. (d) There were correlations between the gut microbiota composition and lipid metabolism indicators. The abundance of Romboutsia was positively correlated with TG and LDL-C levels but negatively correlated with high-density lipoprotein cholesterol (HDL-C) levels. The abundance of Akkermansia was negatively correlated with LDL-C levels, and the abundance of Blautia was negatively correlated with body mass index (BMI) and LDL-C levels.

Conclusions

The intestinal flora diversity varied by body weight and altitude, with lower diversity in those at higher altitudes and with lower body weights. Prevotella likely plays a role in suppressing obesity at high altitudes.

The influence of the urban food environment on diet, nutrition and health outcomes in low-income and middle-income countries: a systematic review

INTRODUCTION

Diet and nutrition are leading causes of global morbidity and mortality. Our study aimed to identify and synthesise evidence on the association between food environment characteristics and diet, nutrition and health outcomes in low-income and middle-income countries (LMICs), relevant to urban settings, to support development and implementation of appropriate interventions.

METHODS

We conducted a comprehensive search of 9 databases from 1 January 2000 to 16 September 2020 with no language restrictions. We included original peer-reviewed observational studies, intervention studies or natural experiments conducted in at least one urban LMIC setting and reporting a quantitative association between a characteristic of the food environment and a diet, nutrition or health outcome. Study selection was done independently in duplicate. Data extraction and quality appraisal using the National Heart Lung and Blood Institute checklists were completed based on published reports using a prepiloted form on Covidence. Data were synthesised narratively.

RESULTS

74 studies met eligibility criteria. Consistent evidence reported an association between availability characteristics in the neighbourhood food environment and dietary behaviour (14 studies, 10 rated as good quality), while the balance of evidence suggested an association with health or nutrition outcomes (17 of 24 relevant studies). We also found a balance of evidence that accessibility to food in the neighbourhood environment was associated with diet (10 of 11 studies) although evidence of an association with health outcomes was contradictory. Evidence on other neighbourhood food environment characteristics was sparse and mixed. Availability in the school food environment was also found to be associated with relevant outcomes. Studies investigating our other primary outcomes in observational studies of the school food environment were sparse, but most interventional studies were situated in schools. We found very little evidence on how workplace and home food environments are associated with relevant outcomes. This is a substantial evidence gap.

CONCLUSION

'Zoning' or 'healthy food cart' interventions to alter food availability may be appropriate in urban LMIC.

PROSPERO REGISTRATION NUMBER

CRD42020207475.