Residential exposure to fast-food restaurants and its association with diet quality, overweight and obesity in the Netherlands: a cross-sectional analysis in the EPIC-NL cohort

Identifying obesogenic environment through spatial clustering of body mass index among adults

BACKGROUND

The escalating trend of obesity in Malaysia is surmounting, and the lack of evidence on the environmental influence on obesity is untenable. Obesogenic environmental factors often emerge as a result of shared environmental, demographic, or cultural effects among neighbouring regions that impact lifestyle. Employing spatial clustering can effectively elucidate the geographical distribution of obesity and pinpoint regions with potential obesogenic environments, thereby informing public health interventions and further exploration on the local environments. This study aimed to determine the spatial clustering of body mass index (BMI) among adults in Malaysia.

METHOD

This study utilized information of respondents aged 18 to 59 years old from the National Health and Morbidity Survey (NHMS) 2014 and 2015 at Peninsular Malaysia and East Malaysia. Fast food restaurant proximity, district population density, and district median household income were determined from other sources. The analysis was conducted for total respondents and stratified by sex. Multilevel regression was used to produce the BMI estimates on a set of variables, adjusted for data clustering at enumeration blocks. Global Moran's I and Local Indicator of Spatial Association statistics were applied to assess the general clustering and location of spatial clusters of BMI, respectively using point locations of respondents and spatial weights of 8 km Euclidean radius or 5 nearest neighbours.

RESULTS

Spatial clustering of BMI independent of individual sociodemographic was significant (p < 0.001) in Peninsular and East Malaysia with Global Moran's index of 0.12 and 0.15, respectively. High-BMI clusters (hotspots) were in suburban districts, whilst the urban districts were low-BMI clusters (cold spots). Spatial clustering was greater among males with hotspots located closer to urban areas, whereas hotspots for females were in less urbanized areas.

CONCLUSION

Obesogenic environment was identified in suburban districts, where spatial clusters differ between males and females in certain districts. Future studies and interventions on creating a healthier environment should be geographically targeted and consider gender differences.

Assessing the healthiness of menus of all out-of-home food outlets and its socioeconomic patterns in Great Britain

Food environment research predominantly focuses on the spatial distribution of out-of-home food outlets. However, the healthiness of food choices available within these outlets has been understudied, largely due to resource constraints. In this study, we propose an innovative, low-resource approach to characterise the healthiness of out-of-home food outlets at scale. Menu healthiness scores were calculated for food outlets on JustEat, and a deep learning model was trained to predict these scores for all physical out-of-home outlets in Great Britain, based on outlet names. Our findings highlight the "double burden" of the unhealthy food environment in deprived areas where there tend to be more out-of-home food outlets, and these outlets tend to be less healthy. This methodological advancement provides a nuanced understanding of out-of-home food environments, with potential for automation and broad geographic application.

The association between the food environment and adherence to healthy diet quality: the Maastricht Study

OBJECTIVE

The purpose of this study is to determine if healthier neighbourhood food environments are associated with healthier diet quality.

DESIGN

This was a cross-sectional study using linear regression models to analyse data from the Maastricht Study. Diet quality was assessed using data collected with a FFQ to calculate the Dutch Healthy Diet (DHD). A buffer zone encompassing a 1000 m radius was created around each participant home address. The Food Environment Healthiness Index (FEHI) was calculated using a Kernel density analysis within the buffers of available food outlets. The association between the FEHI and the DHD score was analysed and adjusted for socio-economic variables.

SETTING

The region of Maastricht including the surrounding food retailers in the Netherlands.

PARTICIPANTS

7367 subjects aged 40-75 years in the south of the Netherlands.

RESULTS

No relationship was identified between either the FEHI (B = 0·62; 95 % CI = -2·54, 3·78) or individual food outlets, such as fast food (B = -0·07; 95 % CI = -0·20, 0·07) and diet quality. Similar null findings using the FEHI were identified at the 500 m (B = 0·95; 95 % CI = -0·85, 2·75) and 1500 m (B = 1·57; 95 % CI = -3·30, 6·44) buffer. There was also no association between the food environment and individual items of the DHD including fruits, vegetables and sugar-sweetened beverages.

CONCLUSION

The food environment in the Maastricht area appeared marginally unhealthy, but the differences in the food environment were not related to the quality of food that participants reported as intake.

Can mindful eating buffer against the influence of neighbourhood fast food exposure on unhealthy food intake?

A food-abundant environment is associated with unhealthy food intake, but not everyone is affected to the same degree. Mindful eating, which is eating with attention and awareness, has been associated with less external eating and less food cravings, and could act as a protective factor against influences from the food environment. The current study aimed to investigate whether the association between exposure to fast-food around the home and unhealthy food intake was moderated by mindful eating. The study was conducted in 1086 Dutch adults of 55 years and older of the Longitudinal Aging Study Amsterdam study. The mindful eating domains (Mindful Eating Behavior Scale) were tested as moderating variables in the linear regression models with absolute and relative density of fast-food outlets in the neighbourhood (400, 800 and 1600m) as independent variables and unhealthy food intake (snacks (g/d)) and saturated fat as a percentage of total energy intake (en%)) as dependent variable. Bootstrapping with 5000 samples using the pick-a-point approach showed that after adjustments, only two out of 48 interactions terms were statistically significant: Eating with Awareness (EwA) and Eating without Distraction (EwD) moderated the positive association between the relative density of fast-food outlets and saturated fat (en%) respectively in a buffer of 800m (interaction EwA: B = -0.84, 95% CI [-1.46; -0.22]) and in a buffer of 1600m (interaction EwD: B = -0.82, 95% CI [-1.61; -0.04]). The results of the current study indicate that mindful eating cannot buffer against the influence of the fast-food abundant environment on unhealthy food intake. Future research is needed to confirm these findings, for example in younger populations.

Survey-derived activity space-based exposures to fast food outlets and their cross-sectional associations with use of fast food outlets, diet quality and BMI

There is a need for conceptual and methodological innovation in food environment-health research. We compared different operationalizations of survey-derived activity space exposures to fast food outlets (FFOs) in associations with use of FFO, diet quality and body mass index (BMI). FFO exposure was determined for home, work and a maximum of sixteen other locations reported by 1728 Dutch adults. Considerable differences in count of FFO between locations were found. Adjusted linear regression analyses resulted in small, unexpected associations with use of FFO, diet quality and BMI, whereby the strength of associations differed between exposure measures. Using home and work areas may be a cost-efficient compromise to capture large parts of the exposure to FFOs.

The association between fast-food outlet proximity and density and Body Mass Index: Findings from 147,027 Lifelines Cohort Study participants

Unhealthy food environments may contribute to an elevated Body Mass Index (BMI), which is a chronic disease risk factor. We examined the association between residential fast-food outlet exposure, in terms of proximity and density, and BMI in the Dutch adult general population. Additionally, we investigated to what extent this association was modified by urbanisation level. In this cross-sectional study, we linked residential addresses of baseline adult Lifelines Cohort participants (n = 147,027) to fast-food outlet locations using geo-coding. We computed residential fast-food outlet proximity, and density within 500 m, 1, 3, and 5 km. We used stratified (urban versus rural areas) multilevel linear regression models, adjusting for age, sex, partner status, education, employment, neighbourhood deprivation, and address density. The mean BMI of participants was 26.1 (SD 4.3) kg/m². Participants had a mean (SD) age of 44.9 (13.0), 57.3% was female, and 67.0% lived in a rural area. Having two or more (urban areas) or five or more (rural areas) fast-food outlets within 1 km was associated with a higher BMI (B = 0.32, 95% confidence interval (CI): 0.03, 0.62; B = 0.23, 95% CI: 0.10, 0.36, respectively). Participants in urban and rural areas with a fast-food outlet within <250 m had a higher BMI (B = 0.30, 95% CI: 0.03, 0.57; B = 0.20, 95% CI: 0.09, 0.31, respectively). In rural areas, participants also had a higher BMI when having at least one fast-food outlet within 500 m (B = 0.10, 95% CI: 0.02, 0.18). In conclusion, fast-food outlet exposure within 1 km from the residential address was associated with BMI in urban and rural areas. Also, fast-food outlet exposure within 500 m was associated with BMI in rural areas, but not in urban areas. In the future, natural experiments should investigate changes in the fast-food environment over time.