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

Abstract

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.

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

“…This could have resulted in underestimated associations because participants who did not attend the follow-up visit had a higher BMI than those who did. Besides, we had data available for residential fast- research in Lifelines has shown that the impact of this temporal mismatch is limited [27]. Furthermore, the follow-up period of approximately 4 years was relatively short to assess associations between fast-food environments and BMI change.…”

“…Results may further have been affected by temporal mismatch between measurement of fast‐food exposure (2012) and baseline BMI (2006 to 2013). Still, 85.2% of participants were recruited between 2010 and 2013, and previous research in Lifelines has shown that the impact of this temporal mismatch is limited [27]. Furthermore, the follow‐up period of approximately 4 years was relatively short to assess associations between fast‐food environments and BMI change.…”

“…We linked participants' geocoded residential addresses at baseline to LISA (http://www.lisa.nl), a Dutch register containing locations where paid work is performed for ≥1 h/mo. In line with previous research [27], residential addresses were linked to 2012 LISA data, corresponding with the median recruitment year of the baseline assessment. Using this linkage, we extracted locations of fast‐food outlets, healthy food outlets, and physical activity facilities (Supporting Information Table S1) [27].…”

“…In line with previous research [27], residential addresses were linked to 2012 LISA data, corresponding with the median recruitment year of the baseline assessment. Using this linkage, we extracted locations of fast‐food outlets, healthy food outlets, and physical activity facilities (Supporting Information Table S1) [27]. In addition, Lifelines participants' 2012 residential neighborhood code was linked to 2012 Statistics Netherlands neighborhood data [28].…”

Impact of Healthy Food Consumption Practice on Normal BMI among College Girls

“…[12] Van erpecum et.al found evidence that participants who had one fast-food outlet had a higher BMI than participants with no fast-food outlets. [13] Furthermore Newby PK et,al. detailed in their study that the consumption of meat and potatoes caused an annual change in BMI (0.30+/_ /0.06) and in waist circumference was more than 3 times as great for subjects in the white-bread cluster. [14] Third, the less consumption frequency (1 time or < 1 time / day) of the certain foods such as Juice, energy drinks, coffee, or sweetened beverages was 18.2%, Desserts, like chocolate or ice cream, and other sweet foods was 23.3%, and Fried food/or packaged snacks was 17.6% noted among the college girls (Table 3).…”