Summary
Background
Diabetes is a major health concern and is influenced by lifestyle, which can be affected by the neighbourhood environment. Specifically, a fast-food environment can influence eating behaviours and thus diabetes prevalence. Therefore, our aim was to assess the relationship between fast-food environment and diabetes prevalence for urban and rural environments in the Netherlands, using multiple indicators and buffer sizes.
Methods
In this cross-sectional study, data on a nationwide sample of adults older than 19 years in the Netherlands were taken from the 2012 Dutch national health survey (from Public Health Monitor), in which participants were surveyed on topics related to health and lifestyle behaviour. Fast-food outlet exposures were determined within street-network buffers of 100 m, 400 m, 1000 m, and 1500 m around residential addresses. For each of these buffers, three indicators were calculated: presence (yes or no) of fast-food outlets, fast-food outlet density, and ratio. Logistic regression analyses were carried out to assess associations of these indicators with diabetes, adjusting for potential confounders and stratifying into urban and rural areas.
Findings
387195 adults were surveyed, 284793 of whom were included in the study. 22951 (8%) reported having diabetes. Fast-food outlet exposures were positively associated with diabetes prevalence. We did not observe large differences between urban and rural areas. The effect estimates were small for all indicators. For example, in the 400 m buffer in the urban environment, the odds ratio (OR) for having diabetes among people with a fast-food outlet present compared with those without, was 1006 (95% CI 10031009) using the presence indicator. The presence indicator showed higher effect estimates and the most consistent results across buffer sizes (ranging from OR 1005 [95% CI 10001010] with the 1000 m buffer to 1016 [10051028] with the 1500 m buffer in urban areas and from 1002 [09981005] with the 1500 m buffer to 1009 [10061018] with the 100 m buffer in rural areas) compared with the density and ratio indicators.
Interpretation
The results confirm the evidence that the fast-food outlet environment is a diabetes risk factor. All data included were at the individual level and the variability was ensured by the spatial distribution and number of participants. In this study, we only accounted for residential exposure because we were unable to account for exposure outside the residential environment. The findings of this study encourage local governments to consider the potential adverse effects of fast-food exposures and aim at minimising unhealthy food access.
Funding
Global Geo Health Data Centre, Utrecht University, Netherlands.
Introduction
,
,
Globally, one in ten adults aged 2079 years had diabetes in 2021.
The prevalence of diabetes is estimated to increase even further, from 28% in 2000 to 44% in 2030.
Also, in the Netherlands, the proportion of people prescribed diabetes medication increased from 38 % in 2006 to 46% in 2012.
There are two main types of diabetes that can affect the general population: type 1 diabetes and type 2 diabetes. Type 1 diabetes is almost exclusively determined by genetics and represents less than 10% of the total diabetes cases.
By contrast, type 2 diabetes is highly determined by modifiable, mainly lifestyle-related factors, such as physical inactivity, poor diet, smoking, and alcohol consumption.
,
,
A fast-food environment usually promotes a ready-to-eat meal lifestyle due to convenience and a way to socialise.
Access to fast-food also promotes impulsive eating
and binge eating.
This is because fast-food makes eating easy and fast due to accessibility, availability, and affordability,
and reasoned forethought is usually replaced by dysfunctional impulsivity.
,
In many countries,
,
the number of fast-food outlets is increasing. In the Netherlands, the number of food outlets has increased by 8% from 2008 to 2012.
Given that most fast-food is energy dense and nutrient poor, its consumption might cause an increased body-mass index and type 2 diabetes.
,
,
However, most of the studies are at an aggregated level, comparing area-level diabetes and area-level fast-food exposures.
,
,
,
,
,
,
,
Of the four studies with analyses at the individual level (table 1),
,
,
,
one does not represent general populations.
Furthermore, none of the individual-level studies evaluated differences between urban and rural areas, whereas studies that classified into urban and rural or metropolitan and non-metropolitan found important differences in associations between diabetes and food environment between these areas.
,
There is no consensus on the buffer-size used in individual level studies. The most used buffer-size to measure the food environment was 1 km for the European studies,
,
1 mile (16 km) for the US studies.
,
However, there is evidence that the fast-food environment within smaller buffers (400 m or 500 m) is also related to health outcomes such as cardiovascular disease, obesity,
,
and diabetes.
Finally, only one of the individual-level studies
was done in Europe and at a national level, and is therefore most comparable with the current study. This European study reported small positive associations between fast-food outlet density and diabetes prevalence.
For example, participants exposed to the highest density fast-food restaurant category (>1070 units/km2) reported significantly higher odds of type 2 diabetes (odds ratio [OR] 1112 [95% CI 102121]) compared with participants with no exposure within a 1000 m buffer.
Table 1Summary of individual-level studies examining the associations between fast-food outlet exposures and diabetes
GIS=geographical information system. HR=hazard ratio. OR=odds ratio.
Evidence before this study
We searched PubMed and Google Scholar with no date restrictions. The searched terms we used were: fast food outlet, food exposures, food environment, unhealthy food environment AND diabetes, diabetes type 2, diabetes mellitus. The search was limited to English language publications. From the literature search we found only four studies (two cross-sectional, one longitudinal, and one including both cross-sectional and longitudinal analyses) that have examined the association between the fast-food environment and diabetes at an individual level, although diabetes is a major global health threat. Furthermore, there is no consensus on the buffer size and the indicator used to assess fast-food environment exposure in the studies. None of the individual-level studies evaluated differences between urban and rural areas.
Added value of this study
This study relied on a large, general population and is, to our knowledge, the largest study to examine the associations between the fast-food environment and diabetes prevalence in the Netherlands. All data used were at an individual level. Moreover, we were able to adjust for potential confounders and risk factors, including socioeconomic and lifestyle factors and air pollution, to disentangle the actual effect of fast-food exposures on diabetes. The fast-food exposure indicator and the buffer size are of key importance to be able to judge the evidence. Therefore, to measure the fast-food environment we used several buffer sizes and indicators because there is no standardised method to assess exposures. Fast-food exposures were objectively measured using four different sizes of street-network buffers around residential addresses and classified into urban and rural areas on the basis of the urbanisation level. We found associations between fast-food environment exposures and diabetes prevalence. The magnitude of the associations was more pronounced for the presence indicator, which it showed greater effect estimates than the other indicators.
Implications of all the available evidence
Our findings add to the evidence that the fast-food environment affects diabetes prevalence. The magnitude of the associations varied with selected buffer size and indicator. The findings should motivate further research in fast-food environment exposure assessment. Furthermore, the findings encourage local governments to consider the potential health-related adverse effects of fast-food exposures, given the increasing number of fast-food outlets, and the size of the populations exposed.
Despite the increasing need to identify the relationship between the fast-food environment and diabetes, the scientific evidence is ambiguous as there is no standard way for quantifying the fast-food environment. To address this challenge, we aimed to investigate the associations between fast-food environment exposures and diabetes prevalence by using various exposure indicators and buffer sizes, for rural and urban subpopulations separately.
We hypothesise that greater fast-food exposure will result in elevated diabetes prevalence. It is also hypothesised that for smaller buffers, the associations with diabetes will be stronger in urban areas than in rural areas. Finally, we expect that diabetes prevalence will vary among the fast-food exposure indicators used.
Methods
Study design and population
This is a cross-sectional study among a nationwide sample of adults in the Netherlands and aimed to examine the associations between exposures to the fast-food outlet environment and diabetes. To characterise exposure to the fast-food outlet environment we applied multiple buffer sizes and indicators. Furthermore, the analysis was stratified into urban and rural areas.
on topics related to health and lifestyle behaviour.
The comparison showed that the data are skewed towards the older population by design, with nearly 38% being aged 65 years or older; whereas in the general Dutch population only 16% are aged 65 years or older. Furthermore, people of Dutch origin are overrepresented in the Public Health Monitor (87% compared with 79% in the general Dutch population), whereas people in the lowest household income quintile are underrepresented (9% compared with 20% in the general Dutch population), probably due to differential response rates.
There is no ethics statement for this national study.
Outcome definition and potential confounders
,
,
and included individual characteristics (age, sex, marital status, and country of origin [ie, where the participant or at least one of their parents was born if outside of the Netherlands; we classified Dutch origin as participants with both parents born in the Netherlands]), socioeconomic factors (highest achieved education level, household income, and neighbourhood socioeconomic status), individual lifestyle risk factors (smoking status and smoking intensity, alcohol consumption, and physical activity), and environmental exposures (air pollution).
CBS and RIVM added the mean neighbourhood socioeconomic status of the four-digit postal code area a person lives in, representing educational, occupational, and economical status of a neighbourhood.
We categorised neighbourhood socioeconomic status into quintiles, where the first quintile represents the highest socioeconomic status. For physical activity, we included activities with moderate-intensity metabolic equivalent and higher (score >3). To assess long-term air pollution (NO2) at the home addresses, we used European Study of Cohorts for Air Pollution Effects (ESCAPE maps, described elsewhere
,
), which are land-use regression models. Home address concentrations of NO2 were calculated by applying the land-use regression in PCRaster software using 55 m grids.
The association between air pollution and diabetes is probably due to systematic inflammation or oxidative stress induced by NO2 or particulate matters, with following impact on metabolic pathways.
Health outcomes and individual data were obtained for 2012. The air pollution models were created in 2009.
Fast-food exposure indicators
The 1000 m (10 min walking or 4 min cycling) and 1500 m (15 min walking or 5 min cycling) buffers are the most commonly used buffers in previous research.
We geocoded all Dutch addresses and we applied street network buffers around them. To calculate the buffers, we used PCRaster Python
and Numpy.
Street network buffers represent realistic exposures, which were calculated as the walking distance along the road network with highways removed.
We used various indicators to represent fast-food exposure. The first indicator was the presence (yes or no) of fast-food outlets within certain street network buffer sizes (100 m, 400 m, 1000 m, and 1500 m). The second indicator was the fast-food outlet density indicator within a 400 m, 1000 m, and 1500 m buffer. The density indicator was expressed as the total number of fast-food outlets within certain street-network buffers and was divided into quartiles. Finally, the third indicator was the fast-food outlet ratio, calculated by dividing the number of fast-food outlets by the total number of food outlets within 400 m, 1000 m, and 1500 m buffers, and was also divided into quartiles. We used ratio to represent a net-negative food environment. For example, if only fast-food outlets are present (eg, two) the ratio measure would be high (2/2=1) and therefore indicate an unhealthier environment compared with a case where different outlets were also available (eg, in addition to the two fast food outlets, one grocery store and one supermarket were available, resulting in a ratio of 2/4=05). To calculate the ratio, we excluded all participants living in buffers without any food retailers because it would not be possible to calculate those indicators of exposure. The 100 m buffer was not applied for the density and ratio indicators because it was impossible to compute quartiles for those indicators for the smallest buffer due to the large number of buffers containing no food outlets. By using three different indicators we aimed to identify whether the presence of fast-food outlets has a greater effect on diabetes prevalence than the net-negative (ratio) food environment. Furthermore, the presence of fast-food outlets and fast-food outlet density indicators are absolute indicators, whereas fast-food outlet ratio is a relative indicator.
The linkage between the fast-food dataset and the Public Health Monitor data was based on the participants addresses. The linkage was done in a secured environment and after the linkage the addresses were removed and were substituted by codes to respect data privacy.
Statistical analysis
We built a priori six logistic regression models to obtain the OR and 95% CI of the associations between fast-food outlet exposure and presence of diabetes (yes or no). We adjusted for possible confounders incrementally starting from the completely unadjusted (crude) model (model 0). Model 1 was adjusted for sex and age. Model 2a added individual socioeconomic characteristics (highest achieved education level, household income, marital status, and country of origin) to model 1. Model 2b added lifestyle risk factors (smoking habits, alcohol consumption, and physical activity) to model 2a. Model 3a added neighbourhood socioeconomic status score to model 2b. Model 3b added air pollution to model 3a. We further checked the variance of inflation for the variables included in our models and verified that there was no issue of multicollinearity (variance of inflation <3). Additionally, we did a sensitivity analysis by adjusting for supermarkets and grocery stores, which can be considered to contribute to a beneficial food environment. We also tested for interactions between fast-food outlet density and individual income and education.
CBS includes five categories to define rural areas (2) and the remaining three categories to define urban areas (10002500 addresses within 1 km2).
We entered all indicators of food exposures as categorical variables by defining their categories either based on quartiles or by entering 1 and 0 for presence and absence of fast-food outlets. We used as base category the category with the lowest number for all buffers (100 m, 400 m, 1000 m, and 1500 m). All confounders were specified as categorical variables except the number of cigarettes smoked and alcohol consumption, which were entered as continuous variables. The statistical analyses were done in R version 3.2.2.
Role of the funding source
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or the writing of the report.
Results
Table 2Public Health Monitor participants’ characteristics and fast-food exposures stratified by diabetes prevalence for participants in urban and rural areas
Data are n (%) or mean (SD).
Table 3Associations of fast-food outlet presence with diabetes prevalence in the fully adjusted model in urban areas
The complete model is adjusted for age, sex, marital status, origin, highest achieved education level, household income, neighbourhood socioeconomic status, smoking habits, alcohol consumption, physical activity, and air pollution. The participants column shows the number and proportion of participants in each category. OR=odds ratio.
Table 4Associations of fast-food outlet density and ratio with diabetes prevalence in the fully adjusted model in urban areas
The complete model is adjusted for age, sex, marital status, origin, highest achieved education level, household income, neighbourhood socioeconomic status, smoking habits, alcohol consumption, physical activity, and air pollution. The participants column shows the number and proportion of participants in each category. OR=odds ratio.
Table 5Associations of fast-food outlet presence with diabetes prevalence in the fully adjusted model in rural areas
The complete model is adjusted for age, sex, marital status, origin, highest achieved education level, household income, neighbourhood socioeconomic status, smoking habits, alcohol consumption, physical activity, and air pollution. The participants column shows the number and proportion of participants in each category. OR=odds ratio.
Table 6Associations of fast-food outlet density and ratio with diabetes prevalence in the fully adjusted model in rural areas
The complete model is adjusted for age, sex, marital status, origin, highest achieved education level, household income, neighbourhood socioeconomic status, smoking habits, alcohol consumption, physical activity, and air pollution. The participants column shows the number and proportion of participants in each category. OR=odds ratio.
Discussion
,
,
,
that showed a protective effect with increased street distance to the nearest fast-food outlet. However, it should be noted that compared with the other estimates, we showed a very large effect estimate for the 1500 m buffer for the fast-food outlet presence indicator in urban areas, which is hard to interpret due to the small number of participants with no exposure (2127 [15%] of the population without diabetes and 114 [08%] of the population with diabetes). In contrast with the presence and density indicators, we found greater effect estimates for the larger buffer sizes (1000 m and 1500 m) for the fast-food outlet ratio.
regardless of whether healthier options are available. We further hypothesise that fast-food outlets that require travelling (1015 min on foot or 45 min by bike) do not trigger impulsive eating as much as those that do not require travelling, which might explain the weaker associations found for the larger buffers. However, when there are no alternative food options within the broader environment, the fast-food exposure in the larger buffers seems to be more important than within the smaller buffers. Therefore, the gravity model (the closer the outlet is, the more appealing it gets
) might be more relevant when applying absolute indicators such as fast-food presence and density, and not when applying relative indicators (eg, ratio).
Regarding the urbanrural distinction, we did not find large differences in effects. However, for the density and ratio indicators, we found slightly smaller effect estimates in rural areas than in urban areas, especially for the 400 m and 1000 m buffers. The smaller effects in rural compared with urban areas can be explained by the different structure of the rural areas: rural areas are sparsely populated and the distances to fast-food outlets are greater.
Although the overall findings suggest a possible link between fast-food outlet exposures and diabetes prevalence, the clinical relevance is questionable due to the small effect estimates.
One of the strengths of this study was that it relied on a large, general population for which fast-food exposures were objectively measured. All data were at an individual level, while the variability is ensured by the spatial distribution and the number of the participants. Moreover, we adjusted for potential confounders, including socioeconomic and lifestyle factors and air pollution, to disentangle the actual effect of fast-food exposures on diabetes prevalence. Furthermore, we used four different sizes of street-network buffers around residential addresses. We were also able to classify urban and rural areas based on the urbanisation level. Finally, we used several food exposure indicators because there is no standardised methodology for food exposure indicators.
The small proportion of people younger than 40 years with diabetes within our population is an indication that most of the cases in our cohort are of type 2 diabetes. Finally, comparing the results of the current study with others is difficult because studies of this kind either follow a different design or are carried out in the USA, which has a different urban morphology to the Netherlands. The difficulty in comparing the findings between studies is also due to the use of different indicators and buffer sizes.
Therefore, future studies should explore the relation between fast-food exposure and diabetes by additionally accounting for fast-food exposures in the working environment and during commuting to get the complete individual exposure. Furthermore, longitudinal data would help to investigate diabetes incidence. Finally, based on our findings, buffers smaller than 1 km are also meaningful for studies of this kind and should be used in future research.
Our findings add to the evidence that the fast-food environment is associated with diabetes. The magnitude of the associations varied with selected buffer size and indicator. We observed the strongest association for the fast-food presence indicator. We did not observe clear differences in effect estimates between urban and rural areas. The findings encourage local governments to consider the potential health-related adverse effects of fast-food exposures, given the increasing number of fast-food outlets, and the size of the population exposed.
Contributors
A-MN conceived the manuscript. A-MN, OS, ML prepared,
accessed, and verified the environmental data. A-MN and MS prepared, accessed, and verified the health data. A-MN, MS, OS, and ML had full access to the initial datasets. The dataset linkage was done in a Central Bureau Statistics of the Netherlands secured environment with access granted to a limited number of people due to the inclusion of sensitive personal data: A-MN and IV had full access to the final dataset and DK could observe the data without entering the secured environment himself. A-MN wrote the initial draft. MP, DK, IV, GH, NJ, and DEG did a critical revision of the manuscript. A-MN did the initial analyses. MP, DK, IV, and GH provided important feedback on how the study can be improved. All authors read and approved the final manuscript and had final responsibility for the decision to submit for publication.
Data sharing
The datasets generated or analysed during the current study are not publicly available due to the sensitive nature of the raw data, but the calculated exposures are available from the corresponding author on reasonable request.
Declaration of interests
We declare no competing interests.
Acknowledgments
We thank Statistics Netherlands (www.cbs.nl/en-GB) and Locatus BV (www.locatus.com) for the use of the databases.
Supplementary Material
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