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Food Outlet Availability and Change in Body Mass Index: a Longitudinal Observational Study of Women from Victoria, Australia

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Food Outlet Availability and Change in Body Mass Index: a Longitudinal Observational Study of Women from Victoria, Australia Open Access Research BMJ Open: first published as 10.1136/bmjopen-2017-016594 on 16 October 2017. Downloaded from Associations between major chain fast- food outlet availability and change in body mass index: a longitudinal observational study of women from Victoria, Australia Karen E Lamb,1 Lukar E Thornton,1 Dana Lee Olstad,1 Ester Cerin,2,3 Kylie Ball1 To cite: Lamb KE, Thornton LE, ABSTRACT Strengths and limitations of this study Olstad DL, et al. Associations Objectives The residential neighbourhood fast-food between major chain fast- environment has the potential to lead to increased levels ► This study is one of very few longitudinal studies food outlet availability and of obesity by providing opportunities for residents to change in body mass index: of its kind and enables examination of how major consume energy-dense products. This longitudinal study a longitudinal observational chain fast-food outlet availability is associated with aimed to examine whether change in body mass index study of women from change in body mass index (BMI). (BMI) differed dependent on major chain fast-food outlet Victoria, Australia. BMJ Open ► The collection of major chain fast-food outlet availability among women residing in disadvantaged 2017;7:e016594. doi:10.1136/ data at a follow-up wave enabled examination neighbourhoods. bmjopen-2017-016594 of associations between change in exposure and Setting Eighty disadvantaged neighbourhoods in Victoria, Prepublication history and change in BMI. ► Australia. additional material for this paper ► Loss to follow-up may have limited generalisability Participants Sample of 882 women aged 18–46 years at are available online. To view of findings beyond the sample. However, descriptive baseline (wave I: 2007/2008) who remained at the same please visit the journal (http:// characteristics for major chain fast food availability residential location at all three waves (wave II: 2010/2011; dx. doi. org/ 10. 1136/ bmjopen- and BMI at baseline were comparable for those who wave III: 2012/2013) of the Resilience for Eating and 2017- 016594). did and did not participate in all waves of Resilience Activity Despite Inequality study. for Eating and Activity Despite Inequality. Received 26 February 2017 Primary outcome BMI based on self-reported height and ► BMI was based on self-reported height and weight http://bmjopen.bmj.com/ Revised 24 August 2017 weight at each wave. rather than objective measurements. Accepted 31 August 2017 Results There was no evidence of an interaction between time and the number of major chain fast-food outlets within 2 (p=0.88), 3 (p=0.66) or 5 km (p=0.24) in the multilevel models of BMI. Furthermore, there was no is associated with a number of comorbidi- evidence of an interaction between time and change in ties, the high prevalence of overweight and availability at any distance and BMI. obesity in Australian adults poses problems Conclusions Change in BMI was not found to differ by residential major chain fast-food outlet availability for the long-term health of the nation and places an enormous economic burden on the on September 29, 2021 by guest. Protected copyright. among Victorian women residing in disadvantaged 4 5 neighbourhoods. It may be that exposure to fast-food health system. outlets around other locations regularly visited influence While individual factors, both behavioural 6 change in BMI. Future research needs to consider what and genetic, have known links to over- environments are the key sources for accessing and weight and obesity, there is a growing recog- 1 consuming fast food and how these relate to BMI and nition that the built environment also plays School of Exercise and Nutrition obesity risk. 7 8 Sciences, Deakin University, a role. As outlined in ecological models of 9 10 Institute for Physical Activity health behaviour, the residential neigh- and Nutrition (IPAN), Geelong, bourhood food environment has the poten- Victoria, Australia 2 INTRODUCTION tial to influence individual dietary behaviours Australian Catholic University, by, for example, offering the opportunity to Institute for Health and Ageing, Obesity is a major public health concern, Melbourne, Victoria, Australia with WHO reporting that an estimated 35% purchase affordable healthy products in local 3School of Public Health, The of adults are classed as overweight and 11% supermarkets or by providing options for resi- University of Hong Kong, Hong as obese worldwide.1 In Australia, approx- dents to buy inexpensive energy-dense prod- Kong, China imately 60% of adults are now overweight ucts at fast-food outlets. The proliferation of 2 Correspondence to or obese, with higher prevalence among fast-food outlets has potentially normalised Dr Karen E Lamb; those in socioeconomically disadvantaged the consumption of energy-dense products karen. lamb@ deakin. edu. au and rural communities.3 As excess of weight which may influence body mass index (BMI). Lamb KE, et al. BMJ Open 2017;7:e016594. doi:10.1136/bmjopen-2017-016594 1 Open Access BMJ Open: first published as 10.1136/bmjopen-2017-016594 on 16 October 2017. Downloaded from This may lead to higher levels of obesity, particularly METHODS among those residing in disadvantaged neighbourhoods Study participants were from the Resilience for Eating given research showing higher fast-food outlet availability and Activity Despite Inequality (READI) cohort. Full in these neighbourhoods.11 details of the sampling procedures are provided else- In the USA, researchers have proposed zoning laws where.29 In brief, in 2007, women aged between 18 and that restrict the location of fast-food outlets, compa- 46 years were sampled using the Australian electoral roll rable to those created for alcohol outlets, to help curb (it is compulsory for Australian citizens to register on the the obesity epidemic. These include setting minimum electoral roll). Sampling took place within 40 urban and distances for fast-food outlets from youth-orientated 40 rural randomly selected socioeconomically disadvan- facilities or limiting the number of fast-food outlets in taged suburbs. Suburbs were considered disadvantaged if an area.12 13 Similarly, recent consultations with senior they were in the bottom third of the Australian Bureau representatives in government departments in Australia of Statistics Index of Relative Socio-economic Disadvan- have highlighted planning measures involving the tage. In total, 4349 women (39% response rate) were restriction of fast-food outlets as a potential avenue for recruited at wave I (2007/2008) and completed a postal promoting healthier eating environments.14 However, survey. Among women eligible to remain in the study managing fast-food proliferation in Australia is compli- (ie, who remained resident of a READI suburb) and who cated due to Australian planning laws.15 In addition, it is consented to further follow-up (n=3019), 1912 completed unclear how successful regulations such as these would the survey at wave II (2010/2011) and 1560 at wave III be in practice given mixed findings from studies exam- (2012/2013). ining associations between the residential fast-food envi- Women were eligible for inclusion in this analysis if ronment and BMI or obesity.16 17 While studies in the they participated in all three waves of READI (n=1560), UK,18 Canada19 and the USA20–23 have found evidence reported that they were not pregnant at all three waves that availability of fast-food outlets is positively associ- (n=1341) and had reported weight at more than one ated with BMI, others in Australia24 and the USA25 have time point, meaning that within individual change in BMI shown no evidence of an association, while some in could be assessed (n=1302). Participants whose address Australia26 and New Zealand27 have found evidence of could not be geocoded at wave I (n=5) were omitted as exposure variables could not be derived for these partic- negative associations. ipants. Due to data errors (inconsistent date of birth and Although there are several methodological differences very large change of >80 kg in weight between waves), across these studies including the definitions of fast food two further participants were excluded. This resulted in and neighbourhoods, one clear drawback to most studies a sample of 1295 participants. Finally, to avoid possible is their reliance on cross-sectional data. This limits the confounding due to changing residential location, ability to detect temporal associations or to examine with- http://bmjopen.bmj.com/ only women who remained at the same address during in-individual change in BMI or obesity.28 This is important the study duration were examined. Thus, a final sample of as those with higher fast-food outlet availability at base- 882 participants was considered in this study. Descriptive line may have greater increases in BMI over time than statistics for the full sample at wave I, those who completed those with lower availability. Furthermore, longitudinal all waves of data and those who only completed wave I are observational studies of the food environment have the provided in online supplementary table S1. benefit of allowing examination of how changes in the environment can influence changes in health outcomes. Body mass index Those that have a higher increase in fast-food outlets The weight (kg) and height (m) of participants were on September 29, 2021 by guest. Protected copyright. over time may have a larger increase in BMI due to the self-reported at each wave which enabled BMI (kg/m2) increased opportunity to purchase and consume ener- to be calculated. Research has shown close correspon- gy-dense foods. dence between self-reported and objectively measured The aim of this study was to examine longitudinal asso- body weight among Australian women, with no evidence ciations between the fast-food environment and BMI of a difference found between self-report and objectively among women residing in disadvantaged neighbour- measured BMI (mean difference=0.12 kg/m2, 95% CI hoods in Victoria, Australia.
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