The association between the presence of fast-food outlets and BMI:the role of neighbourhood socio-economic status, healthy food outlets, and dietary factors

BACKGROUND: Evidence on the association between the presence of fast-food outlets and Body Mass Index (BMI) is inconsistent. Furthermore, mechanisms underlying the fast-food outlet presence-BMI association are understudied. We investigated the association between the number of fast-food outlets being present and objectively measured BMI. Moreover, we investigated to what extent this association was moderated by neighbourhood socio-economic status (NSES) and healthy food outlets. Additionally, we investigated mediation by frequency of fast-food consumption and amount of fat intake. METHODS: In this cross-sectional study, we used baseline data of adults in Lifelines (N = 149,617). Geo-coded residential addresses were linked to fast-food and healthy food outlet locations. We computed the number of fast-food and healthy food outlets within 1 kilometre (km) of participants' residential addresses (each categorised into null, one, or at least two). Participants underwent objective BMI measurements. We linked data to Statistics Netherlands to compute NSES. Frequency of fast-food consumption and amount of fat intake were measured through questionnaires in Lifelines. Multivariable multilevel linear regression analyses were performed to investigate associations between fast-food outlet presence and BMI, adjusting for individual and environmental potential confounders. When exposure-moderator interactions had p-value < 0.10 or improved model fit (∆AIC ≥ 2), we conducted stratified analyses. We used causal mediation methods to assess mediation. RESULTS: Participants with one fast-food outlet within 1 km had a higher BMI than participants with no fast-food outlet within 1 km (B = 0.11, 95% CI: 0.01, 0.21). Effect sizes for at least two fast-food outlets were larger in low NSES areas (B = 0.29, 95% CI: 0.01, 0.57), and especially in low NSES areas where at least two healthy food outlets within 1 km were available (B = 0.75, 95% CI: 0.19, 1.31). Amount of fat intake, but not frequency of fast-food consumption, explained this association for 3.1%. CONCLUSIONS: Participants living in low SES neighbourhoods with at least two fast-food outlets within 1 km of their residential address had a higher BMI than their peers with no fast-food outlets within 1 km. Among these participants, healthy food outlets did not buffer the potentially unhealthy impact of fast-food outlets. Amount of fat intake partly explained this association. This study highlights neighbourhood socio-economic inequalities regarding fast-food outlets and BMI

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(m), 1, 3, and 5 km(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

Fast-food environments and BMI changes in the Dutch adult general population:the Lifelines cohort

OBJECTIVE: This study investigated cross-sectional and longitudinal associations of fast-food outlet exposure with BMI and BMI change, as well as moderation by age and genetic predisposition.METHODS: This study used Lifelines' baseline (n = 141,973) and 4-year follow-up (n = 103,050) data. Participant residential addresses were linked to a register with fast-food outlet locations (Nationwide Information System of Workplaces [Dutch: Landelijk Informatiesysteem van Arbeidsplaatsen, LISA]) using geocoding, and the number of fast-food outlets within 1 km was computed. BMI was measured objectively. A weighted BMI genetic risk score was computed, representing overall genetic predisposition toward elevated BMI, based on 941 single-nucleotide polymorphisms genome-wide significantly associated with BMI for a subsample with genetic data (BMI: n = 44,996; BMI change: n = 36,684). Multivariable multilevel linear regression analyses and exposure-moderator interactions were tested.RESULTS: Participants with ≥1 fast-food outlet within 1 km had a higher BMI (B [95% CI]: 0.17 [0.09 to 0.25]), and those with ≥2 fast-food outlets within 1 km increased more in BMI (B [95% CI]: 0.06 [0.02 to 0.09]) than participants with no fast-food outlets within 1 km. Effect sizes on baseline BMI were largest among young adults (age 18-29 years; B [95% CI]: 0.35 [0.10 to 0.59]) and especially young adults with a medium (B [95% CI]: 0.57 [-0.02 to 1.16]) or high genetic risk score (B [95% CI]: 0.46 [-0.24 to 1.16]).CONCLUSIONS: Fast-food outlet exposure was identified as a potentially important determinant of BMI and BMI change. Young adults, especially those with a medium or high genetic predisposition, had a higher BMI when exposed to fast-food outlets.</p

Effects of changes in residential fast-food outlet exposure on Body Mass Index change:longitudinal evidence from 92,211 Lifelines participants

BACKGROUND: Evidence on the association between fast-food outlet exposure and Body Mass Index (BMI) remains inconsistent and is primarily based on cross-sectional studies. We investigated the associations between changes in fast-food outlet exposure and BMI changes, and to what extent these associations are moderated by age and fast-food outlet exposure at baseline.METHODS: We used 4-year longitudinal data of the Lifelines adult cohort (N = 92,211). Participant residential addresses at baseline and follow-up were linked to a register containing fast-food outlet locations using geocoding. Change in fast-food outlet exposure was defined as the number of fast-food outlets within 1 km of the residential address at follow-up minus the number of fast-food outlets within 1 km of the residential address at baseline. BMI was calculated based on objectively measured weight and height. Fixed effects analyses were performed adjusting for changes in covariates and potential confounders. Exposure-moderator interactions were tested and stratified analyses were performed if p &lt; 0.10.RESULTS: Participants who had an increase in the number of fast-food outlets within 1 km had a greater BMI increase (B(95% CI): 0.003 (0.001,0.006)). Decreases in fast-food outlet exposure were not associated with BMI change (B(95% CI): 0.001 (-0.001,0.004)). No clear moderation pattern by age or fast-food outlet exposure at baseline was found.CONCLUSIONS: Increases in residential fast-food outlet exposure are associated with BMI gain, whereas decreases in fast-food outlet exposure are not associated with BMI loss. Effect sizes of increases in fast-food outlet exposure on BMI change were small at individual level. However, a longer follow-up period may have been needed to fully capture the impact of increases in fast-food outlet exposure on BMI change. Furthermore, these effect sizes could still be important at population level considering the rapid rise of fast-food outlets across society. Future studies should investigate the mechanisms and changes in consumer behaviours underlying associations between changes in fast-food outlet exposure and BMI change.</p

Neighborhood socioeconomic differences in BMI: The role of fast-food outlets and physical activity facilities

Objective: The goal of this study was to investigate the association between neighborhood socioeconomic status (NSES) and BMI and to what extent this association is moderated by availability of fast-food (FF) outlets and pay-for-use physical activity (PA) facilities. Methods: Baseline data of adults in Lifelines (N = 146,629) were linked to Statistics Netherlands and a register using geocoding to compute, respectively, NSES (i.e., low, middle, high) and the number of FF outlets and PA facilities within 1 km of the residential address. Multivariable multilevel linear regression analyses were performed to examine the association between NSES and BMI. Two-way and three-way interaction terms were tested to examine moderation by FF outlets and PA facilities. Results: Participants living in low NSES areas had a higher BMI than participants living in high (B [95% CI]: 0.76 [0.65 to 0.87]) or middle NSES areas (B [95% CI]: 0.40 [0.28 to 0.51]), independent of individual socioeconomic status. Although two- and three-way interactions between NSES, FF outlets, and PA facilities were significant, stratified analyses did not show consistent moderation patterns. Conclusions: People living in lower NSES areas had a higher BMI, independent of their individual socioeconomic status. The study found no clear moderation of FF outlets and PA facilities. Environmental factors that may mitigate NSES differences in BMI should be the subject of future research

20 mph speed limits: a meta-narrative evidence synthesis of the public health evidence

Twenty mile per hour (20 mph) or 30 kph speed limit interventions are increasingly common in a wide range of European cities. Importantly, 20 mph or 30 kph speed limit interventions may not only reduce road danger, but also positively contribute to broader public health outcomes, such as active travel, play, and air quality. The aim of this chapter is to provide an updated review on the effects of 20 mph speed limits on a range of public health outcomes. To this end, electronic databases of academic literature were searched as well as the grey literature, including MEDLINE, EMBASE, Web of Science and Transport Research Information Service. Databases were searched using keywords related to ‘20 mph’ AND ‘health’. Thirteen academic studies and ten grey literature reports met the inclusion criteria. Clear impacts of 30 kph on reduced injuries, collisions and casualties were consistently reported, whereas less evidence is available on wider health impacts such as changes in active travel, play, and air quality. Few subgroup analyses were reported by age, gender, neighbourhood deprivation level, sex, ethnic minority, work status, disability, or type of user (e.g., cyclist, motorcyclist, car user, or pedestrian). Simultaneously, methodological challenges of existing studies were identified and discussed, including the challenge to assess wider health impacts of 20 mph speed limits and the difficulty in evaluating these limits using experimental methods, whilst also recognizing the system-wide context in which limits are introduced. Future studies should address these challenges and account for the broader role that lower speed limits can play. Without this, lower speed limits research will not unlock the full potential that lower speed limits may play in creating healthier, more inclusive, and more sustainable living environments.<br/