BMC Medicine ( IF 7.0 ) Pub Date : 2020-03-18 , DOI: 10.1186/s12916-020-01538-5 Matthew Hobbs 1 , Duncan Radley 2
Since 1980, the prevalence of obesity has doubled in more than 70 countries [1]. Worldwide, in 2015, the prevalence of children and adults with obesity was 5 and 12%, respectively. This equates to 107.7 million children and 603.7 million adults [1]. The physical and psychological consequences of obesity are well documented, including an increased risk of type 2 diabetes, adverse cardiovascular outcomes, discrimination and reduced self-esteem. Moreover, it was estimated that obesity accounted for approximately 4 million deaths and 120 million disability-adjusted life-years worldwide in 2015 [1].
The relationship between our health and the environment or places in which we reside and work, day to day, dates back centuries. It was Hippocrates who first argued that health was a product of environmental factors and highlighted a need for harmony between the individual, social and natural environment. Fast-forward to the present day and the term ‘obesogenic environment’ has been coined to refer to the influences that the surroundings, opportunities or conditions of life have on promoting obesity in individuals and populations [2]. While the causes of obesity are complex and obesity is multifaceted in aetiology, it is plausible that the condition is driven largely by environmental factors, which undermine the self-regulatory capacity that people have to make responsible decisions about personal diet and physical activity [3]. For instance, it is likely that the increased availability, accessibility and affordability of energy-dense foods, along with intense marketing of such foods, are examples of such environmental factors that, at least partly, explain excess energy intake and weight gain [4].
In recent decades, many researchers have attempted to unpick the relationship between physical and social environment and how these affect people’s weight status. However, the evidence base is inundated with different approaches, methods, metrics and environmental variables, making comparison between studies difficult [5], the search for unequivocal evidence elusive and the translation of evidence into policy near impossible. Highlighted in a recent systematic review of 113 studies, null associations dominated across all measurement methods, comprising 76% of 1937 associations in total [5]. The accompanying comprehensive appraisal of study quality indicated that, in general, study quality and methodological reporting were poor and study findings were at risk of bias. For instance, only three of the included papers (2.7%) contained all relevant details on how food outlets were geocoded. In addition to these methodological limitations, current evidence often relies on static definitions of exposure; for instance, around a participant’s residential home address [6, 7]. This is now well known as the ‘Uncertain Geographic Context Problem’. Recently, Zhao et al. [7] showed that the contextual areas used to derive a particular environmental variable affect whether or not an environmental variable has a significant influence on participants’ body mass index. Indeed, using global positioning systems to track movement has indicated that research often assumes that children and adults are less mobile than they really are. Further, most studies investigating links between environmental factors, such as fast-food outlets and obesity, are cross-sectional [5], making any causal associations unclear.
The paper presented by Wilding et al. [8] provides a unique and important longitudinal addition to current evidence. Using a population-based cohort in the south of England, the authors examine how environmental characteristics, including greenspace, walkability, supermarket density, unhealthy food outlet relative density, spaces for social interaction and air quality at birth are associated with overweight and obesity in school-aged children (14,084 children aged 4–5 years and 5637 aged 10–11 years). It is particularly important to consider these age groups of children given recent evidence from the English 2018/19 National Childhood Measurement Programme, which showed 9.7% of reception class children (aged 4–5 years) were obese, while the prevalence of obesity in year 6 children (aged 10–11 years) was 20.2% [9]. This use of a large dataset of routine data reduces the risk of sampling bias and increases the power to detect meaningful associations between exposure and change in the outcome. Of note, the authors assigned area characteristics on an annual basis (except for walkability) to maximise the relevance of estimated exposures. This considerable methodological effort should be commended.
The use of longitudinal data should also be recognised, since many research studies can only aspire to such longitudinal exposure measures. Furthermore, by conducting subanalysis of children who moved home lower super output area between birth and weight measurement, the study was strengthened by accounting for population migration. This is important because longitudinal designs can address residential self-selection bias by establishing temporality [10]; accounting – for instance – for residential relocation that may be triggered by events such as marriage or employment changes, which may also influence health-related behaviours and subsequent health outcomes [10]. The study concludes that increased access to greenspace and the subsequent protection of greenspace may have a role in early prevention of childhood obesity. While effects were often small, these are likely to be meaningful in effect size when considered at a population level. Future research may wish to consider confounders that the authors recognised, but were unable to adjust for, including paternal factors, maternal diet in pregnancy, parental diet and physical activity, family income, child’s diet and physical activity.
While there is much work to be done to better understand how the environment in which we reside and work affects both our behaviours and health, Wilding et al. (8) provide an important contribution to the current state of evidence. There continues to be growing interest in the environmental determinants of health outcomes and health behaviours. However, as we outline, there are several notable strengths that can be taken from this article in future research. We hope these strengths are recognised and will be considered and incorporated in the development of future research, where feasible.
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- Health Sciences, University of Canterbury, Private Bag 4800, Christchurch, 8140, New Zealand
- Matthew Hobbs
- Applied Obesity Research Centre, Leeds Beckett University, Leeds, LS6 3QT, UK
- Duncan Radley
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MH led the write up of the publication with the support of DR. Both authors read and approved the final manuscript.
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Correspondence to Duncan Radley.
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Hobbs, M., Radley, D. Obesogenic environments and obesity: a comment on ‘Are environmental area characteristics at birth associated with overweight and obesity in school-aged children? Findings from the SLOPE (Studying Lifecourse Obesity PrEdictors) population-based cohort in the south of England’. BMC Med 18, 59 (2020). https://doi.org/10.1186/s12916-020-01538-5
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DOI: https://doi.org/10.1186/s12916-020-01538-5
Keywords
- Health geography
- Obesity
- Environment
- Greenspace
- Cohort study
中文翻译：
致肥环境和肥胖：对“出生时的环境区域特征与学龄儿童超重和肥胖是否相关？”的评论来自英格兰南部 SLOPE（研究生命历程肥胖预测因素）人群的研究结果。
自 1980 年以来，70 多个国家的肥胖患病率增加了一倍[1]。 2015年，全球儿童和成人肥胖患病率分别为5%和12%。这相当于 1.077 亿儿童和 6.037 亿成人[1]。肥胖对身体和心理的影响已有充分记录，包括 2 型糖尿病风险增加、心血管不良后果、歧视和自尊心下降。此外，据估计，2015 年，肥胖导致全球约 400 万人死亡和 1.2 亿伤残调整生命年[1]。
我们的健康与我们日常居住和工作的环境或地点之间的关系可以追溯到几个世纪前。希波克拉底首先提出健康是环境因素的产物，并强调个人、社会和自然环境之间和谐的必要性。快进到今天，“致肥胖环境”一词已被创造出来，指的是周围环境、机会或生活条件对促进个人和人群肥胖的影响[2]。虽然肥胖的原因很复杂，而且肥胖的病因是多方面的，但这种情况很可能主要是由环境因素驱动的，环境因素破坏了人们必须对个人饮食和身体活动做出负责任的决定的自我调节能力[3] 。例如，能量密集食品的可用性、可获取性和可负担性的增加，以及此类食品的密集营销，可能是此类环境因素的例子，至少部分解释了能量摄入过多和体重增加[4] 。
近几十年来，许多研究人员试图解开物理和社会环境之间的关系，以及它们如何影响人们的体重状况。然而，证据基础充斥着不同的途径、方法、指标和环境变量，使得研究之间的比较变得困难[5]，寻找明确的证据难以捉摸，将证据转化为政策几乎不可能。最近对 113 项研究进行的系统回顾强调，无效关联在所有测量方法中占主导地位，总共占 1937 项关联的 76% [5]。随附的研究质量综合评估表明，总体而言，研究质量和方法报告较差，研究结果存在偏倚风险。例如，纳入的论文中只有三篇 (2.7%) 包含有关如何对食品店进行地理编码的所有相关详细信息。除了这些方法论的局限性之外，当前的证据通常依赖于暴露的静态定义。例如，围绕参与者的家庭住址 [6, 7]。这现在被称为“不确定的地理背景问题”。最近，赵等人。 [7]表明，用于推导特定环境变量的背景区域会影响环境变量是否对参与者的体重指数产生显着影响。事实上，使用全球定位系统来跟踪运动表明，研究通常假设儿童和成人的活动能力比实际情况要差。此外，大多数调查环境因素（例如快餐店和肥胖）之间联系的研究都是横向的[5]，使得任何因果关系都不清楚。
Wilding 等人提出的论文。 [8]为当前证据提供了独特且重要的纵向补充。作者利用英格兰南部的人口队列研究了环境特征（包括绿地、步行能力、超市密度、不健康食品店相对密度、社交空间和出生时空气质量）与学校超重和肥胖的关系。 - 年龄儿童（4-5 岁儿童 14,084 名，10-11 岁儿童 5637 名）。鉴于英国 2018/19 年度国家儿童测量计划的最新证据，考虑这些年龄组的儿童尤为重要，该计划显示 9.7% 的接待班儿童（4-5 岁）肥胖，而 2018 年肥胖患病率6名儿童（10-11岁）为20.2%[9]。使用大型常规数据集可以降低抽样偏差的风险，并提高检测暴露与结果变化之间有意义关联的能力。值得注意的是，作者每年分配区域特征（步行适宜性除外），以最大限度地提高估计暴露量的相关性。这种在方法论上所做的巨大努力应该受到赞扬。
纵向数据的使用也应该得到认可，因为许多研究只能追求这种纵向暴露测量。此外，通过对出生和体重测量之间搬家较低超输出区域的儿童进行子分析，通过考虑人口迁移来加强该研究。这很重要，因为纵向设计可以通过建立时间性来解决住宅自我选择偏差[10]；例如，婚姻或就业变化等事件可能引发的住宅搬迁的会计，这也可能影响与健康相关的行为和随后的健康结果[10]。该研究的结论是，增加绿地的使用机会以及随后对绿地的保护可能对儿童肥胖的早期预防发挥作用。虽然影响通常很小，但从人口水平考虑时，这些影响的大小可能是有意义的。未来的研究可能希望考虑作者认识到但无法调整的混杂因素，包括父亲因素、母亲怀孕期间的饮食、父母的饮食和身体活动、家庭收入、儿童的饮食和身体活动。
虽然要更好地了解我们居住和工作的环境如何影响我们的行为和健康还有很多工作要做，Wilding 等人。 (8) 对证据的现状做出重要贡献。人们对健康结果和健康行为的环境决定因素越来越感兴趣。然而，正如我们概述的那样，在未来的研究中可以从本文中获得一些显着的优势。我们希望这些优势得到认可，并在可行的情况下被考虑并纳入未来研究的发展中。
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Hobbs, M., Radley, D. 致肥环境和肥胖：对“出生时的环境区域特征是否与学龄儿童超重和肥胖相关？”的评论来自英格兰南部 SLOPE（研究生命历程肥胖预测因素）人群的研究结果。 BMC 医学18, 59 (2020)。 https://doi.org/10.1186/s12916-020-01538-5
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