[Food environment and its relation to diet behavior and obesity in China]

Association between neighborhood food environment and dietary diversity score among older people in Beijing, China: A cross-sectional study

Objective

To examine the association between the neighborhood food environment and dietary diversity score (DDS) among elderly people in China.

Methods

Participants were recruited from 12 communities in Beijing, China, in 2019, using a multi-stage stratified random sampling method. Participants (n = 1,764, 730 men) in this study were elderly people aged 65 to 80. A questionnaire survey was used to investigate the intake of various foods in the past 3 days, and their dietary diversity score (DDS) was calculated. Baidu Map Application Programming Interface was used to measure the neighborhood food environments, including the density of and proximity to different food outlets. Adjusted multiple linear regression was performed to estimate the association between the neighborhood food environment and DDS.

Results

A total of 1,800 questionnaires were distributed, and 1,775 questionnaires were returned. The questionnaire response rate was 98.6%. Among them, the number of valid questionnaires was 1,764, and the valid rate was 99.4%. The mean age of the participants was 69.7 ± 4.3, and the average DDS was 7.2 ± 1.4. Among the three types of stores, convenience stores had the best access, followed by greengrocers, and finally supermarkets. Sit-down restaurants had the nearest walking distance, Chinese fast-food restaurants had the largest number, and western fast-food restaurants were the most difficult to access. Better access to supermarkets tended to be associated with higher DDS score within all the buffer zones (250 m buffer zone: β = 0.495, P < 0.001; 500 m buffer zone: β = 0.341, P < 0.001; 800 m buffer zone: β = 0.163, P < 0.001; 1,000 m buffer zone: β = 0.243, P < 0.001). However, greengrocers were negatively associated with DDS score within all the buffer zones (250 m buffer zone: β = −0.475, P < 0.001; 500 m buffer zone: β = −0.161, P < 0.001; 800 m buffer zone: β = −0.090, P < 0.001; 1000 m buffer zone: β = −0.112, P < 0.001). As for convenience stores, we only found significant results within the 250 m buffer zone (β = 0.075, P = 0.002). Among the three types of restaurants, the results were inconsistent within different buffer zones. Sit-down restaurants were negatively associated with DDS score within 250 m buffer zone (β = −0.257, P < 0.001), and positively associated with DDS score within 1,000 m buffer zone (β = 0.018, P < 0.001). Living in areas with more Chinese fast-food restaurants tended to have higher DDS within 250 m buffer zone (β = 0.357, P < 0.001); however, there was a opposite result within 1,000 m buffer zone (β = −0.044, P < 0.001). Better access to western fast-food restaurants tended to be associated with lower DDS score within 500 m buffer zone (β = −0.235, P < 0.001) and higher DDS score within 1,000 m buffer zone (β = 0.189, P < 0.001). There was a negative correlation between the nearest distance to the supermarket and the DDS score (β = −0.002, P < 0.001), and the nearest distance to the greengrocer was directly positively correlated with the DDS score (β = 0.004, P < 0.001).

Conclusion

This study suggests that supermarkets may increase the DDS score among older adults in Beijing, while greengrocers may reduce the DDS score. However, the current results are not strong enough to draw specific conclusions. Policymakers need to rely on more evidence to make specific policy recommendations.

Attributes of the food and physical activity built environments from the Southern Cone of Latin America

Previous studies have shown the influence of the physical and social environments on the development of obesity and non-communicable diseases (NCD). An obesogenic environment promotes higher dietary energy intakes and sedentary behaviors while limiting opportunities or incentives for active living. This paper presents a dataset with key attributes of the food and physical activity built environment, including green spaces, quality of streets and sidewalks, and different types of food retail outlets in four cities of the Southern Cone of Latin America. A total of 139 representative neighborhoods randomly selected from: Marcos Paz and Bariloche (Argentina), Temuco (Chile) and Canelones-Barros Blancos (Uruguay) were evaluated, where standardized community walks were conducted for direct observation of the built environment. This dataset will contribute valuable data to the evaluation of obesogenic environments in the region, and could be linked to additional ecological information about risk factors for NCDs and socio-economic features from other sources. Understanding environmental influences on cardiovascular risk factors and individual habits may help explain NCD outcomes and plan urban policies.

Association between Neighborhood Food Environment and Body Mass Index among Older Adults in Beijing, China: A Cross-Sectional Study

OBJECTIVE

To investigate the association between the neighborhood food environment and body mass index (BMI) among Chinese older adults.

METHODS

A multi-stage stratified random sampling method was used to recruit participants from 12 communities in Beijing, China, in 2019. Participants (n = 1764, 1034 women) in this study were older adults aged 65 to 80. We collected the participants' basic information, measured their height and weight, and calculated their BMI. Neighborhood food environments were measured by the density of and proximity to different food outlets using the Baidu Map Application Programming Interface. Adjusted multiple linear regression was performed to estimate the association between the food environment and BMI.

RESULTS

Participants had a mean age of 69.7 ± 4.32 years old and an average BMI of 26.3 ± 3.50 kg/m². Among the three types of stores, convenience stores had the easiest access, followed by greengrocers and supermarkets. Sit-down restaurants had the best access among different restaurants, followed by Chinese fast-food restaurants, and western fast-food restaurants had the worst access. Easier access to greengrocers (β = 0.281, p < 0.001) and sit-down restaurants (β = 0.304, p < 0.001) was associated with higher BMI in the 250 m buffer zone. More supermarkets were associated with higher BMI in the 500 m buffer zone (β = 0.593, p < 0.001). Access to convenience stores was positively associated with BMI in the 800 m buffer zone (β = 0.057, p < 0.001). Better access to Chinese fast-food restaurants was associated with higher BMI (β = 0.071, p = 0.001), and better access to western fast- food restaurants was associated with lower BMI (β = -0.400, p < 0.001) in the 1000 m buffer zone. There was a negative association between the nearest distance to greengrocers and BMI (β = -0.004, p < 0.001).

CONCLUSION

Although we found some significant associations between the neighborhood food environment and obesity, the current results are not strong enough to draw specific conclusions. Policymakers will need to rely on more evidence to derive concrete policy recommendations.